System

Abstract

An object of the system according to the embodiment is to propose an appropriate diet content or training method based on an individual physical condition or body shape.SOLUTION: A system according to an embodiment includes a collection unit, an analysis unit, and a proposal unit. The collection unit collects data. The analysis unit analyzes the data collected by the collection unit. The suggestion unit suggests a recommended meal content or training method based on the analysis result obtained by the analysis unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technology has the problem that it is difficult to suggest appropriate dietary and training methods based on an individual's physical condition and body shape.

[0005] The system according to the embodiment aims to propose appropriate dietary content and training methods based on an individual's physical condition and body shape. [Means for solving the problem]

[0006] The system according to the embodiment includes a collection unit, an analysis unit, and a suggestion unit. The collection unit collects data. The analysis unit analyzes the data collected by the collection unit. The suggestion unit suggests recommended dietary content or training methods based on the analysis results obtained by the analysis unit. [Effects of the Invention]

[0007] The system according to the embodiment can suggest appropriate dietary content and training methods based on an individual's physical condition and body shape. [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) An AI personal trainer system according to an embodiment of the present invention collects and analyzes data such as a user's daily diet and exercise habits, as well as weight, body fat, and bone age, which can be obtained using a scale or other device, and proposes optimal dietary and training methods. The AI ​​personal trainer system collects and analyzes data such as a user's daily diet and exercise habits, as well as weight, body fat, and bone age, which can be obtained using a scale or other device, and proposes recommended dietary and training methods for the user's set goals. For example, the AI ​​personal trainer system records the user's daily dietary and exercise habits. For example, the system records details such as what the user ate for breakfast, the calories consumed for lunch, and the contents of dinner. The system also records the type and duration of exercise, such as walking, running, or strength training. This information is submitted to the AI ​​personal trainer. The AI ​​personal trainer system then records data such as the user's weight, body fat, and bone age, which can be obtained using a scale or other device. For example, the user's weight upon waking up, their body fat percentage after exercise, and their bone age, which are measured periodically. This data is also submitted to the AI ​​personal trainer. The AI ​​personal trainer system then analyzes the submitted data and proposes recommended dietary and training methods for the user's set goals. For example, if a user sets a goal of "I want to lose 3 kg in one month," the AI ​​will propose specific meal and exercise plans toward that goal. For example, it may provide advice such as, "Eat a nutritionally balanced meal for breakfast and choose a menu rich in vegetables for lunch." Furthermore, the AI ​​personal trainer system monitors the user's progress and adjusts its advice as necessary. For example, if the user is making steady progress toward their goal, it will instruct them to continue with the plan as is. Conversely, if they are falling behind, it will suggest increasing the amount of exercise. In this way, the user can always receive optimal advice. This allows the AI ​​personal trainer system to easily maintain health at home, without having to go to the gym. This effectively supports users in maintaining their health.For example, even people with limited time, such as busy businessmen or housewives raising children, can manage their health at their own pace. In addition, the advice provided by AI is optimized for each individual user, so effective dieting and health maintenance can be expected.

[0029] The AI ​​personal trainer system according to the embodiment includes a collection unit, an analysis unit, and a suggestion unit. The collection unit collects data such as the user's daily diet and exercise habits, as well as weight, body fat, and bone age, which can be obtained using a scale or other device. For example, the collection unit records in detail what the user ate for breakfast, the calories consumed for lunch, and the contents of dinner. The collection unit can also record exercise habits such as walking, running, and strength training, along with the duration of the exercise. The collection unit also records data such as weight, body fat, and bone age, which can be obtained using a scale. For example, the collection unit records the user's weight upon waking up in the morning, the body fat percentage after exercise, and bone age, which is measured periodically. The analysis unit analyzes the data collected by the collection unit. For example, the analysis unit recommends dietary and training methods based on the collected data for a target value set by the user. The analysis unit can analyze the data using, for example, statistical analysis or a machine learning algorithm. For example, if a user sets a goal of "losing 3 kg in one month," the analysis unit can suggest a specific diet plan and exercise plan to achieve that goal. The suggestion unit recommends dietary and training methods based on the analysis results obtained by the analysis unit. For example, the suggestion unit provides specific advice such as, "Eat a nutritionally balanced meal for breakfast and choose a menu rich in vegetables for lunch." The suggestion unit can also monitor the user's progress and modify the advice as needed. For example, if the user is making steady progress toward their goal, the suggestion unit instructs the user to continue with the plan as is, while if the user is falling behind, the suggestion unit suggests increasing the amount of exercise. This allows the AI ​​personal trainer system according to the embodiment to effectively support users in maintaining their health. For example, even people with limited time, such as busy businessmen or housewives raising children, can manage their health at their own pace. Furthermore, the advice provided by the AI ​​is optimized for each individual user, which can lead to effective dieting and health maintenance.

[0030] The collection unit can collect data on the user's diet or exercise intake for the day, weight or body fat obtained using a scale, and bone age. For example, the collection unit can record in detail what the user ate for breakfast, the calories consumed for lunch, and the contents of dinner. The collection unit can also record exercise details and duration, such as walking, running, and strength training. Furthermore, the collection unit records data on weight, body fat, bone age, and other data obtained using a scale. For example, the collection unit can record the user's weight upon waking up, the body fat percentage after exercise, and bone age measured periodically. By collecting detailed health data on the user, more accurate analysis and recommendations are possible. Some or all of the above-described processing by the collection unit may be performed using, or without, AI. For example, the collection unit can input the user's diet and exercise data into the generation AI and have the generation AI organize and complete the data.

[0031] The analysis unit can analyze the data collected by the collection unit and suggest recommended dietary content or training methods for target numerical values ​​set by the user. For example, the analysis unit can suggest recommended dietary content or training methods based on the collected data for target numerical values ​​set by the user. The analysis unit can analyze the data using, for example, statistical analysis or machine learning algorithms. For example, if a user sets a goal of "losing 3 kilograms in one month," the analysis unit can suggest specific dietary and exercise plans toward that goal. This makes it possible to effectively maintain health by making specific suggestions based on the user's goals. Some or all of the above-mentioned processing by the analysis unit can be performed, for example, using AI or without AI. For example, the analysis unit can input the collected data into a generation AI and have the generation AI analyze the data and generate suggestions.

[0032] The suggestion unit can monitor the user's progress and change the advice as necessary. For example, the suggestion unit monitors the user's progress and modifies the advice as necessary. For example, if the user is making good progress toward the goal, the suggestion unit instructs the user to continue with the plan as is, and conversely, if the user is falling behind the goal, the suggestion unit suggests increasing the amount of exercise. This supports goal achievement by providing flexible advice according to the user's progress. Some or all of the above-mentioned processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can input the user's progress data into the generation AI and cause the generation AI to modify the advice according to the progress.

[0033] The suggestion unit can propose a specific meal plan or exercise plan based on the target values ​​set by the user. For example, the suggestion unit may provide specific advice such as, "Eat a nutritionally balanced meal for breakfast and choose a menu rich in vegetables for lunch." This allows for effective health maintenance by providing a specific plan tailored to the user's goals. Some or all of the above-described processing by the suggestion unit may be performed using, or without, AI. For example, the suggestion unit may input the user's target values ​​and collected data into the generation AI and cause the generation AI to generate a specific meal plan or exercise plan.

[0034] The collection unit can analyze the user's past data collection history and select the optimal collection method. For example, the collection unit can prioritize and suggest data collection methods (such as manual input or voice input) that the user has frequently used in the past. The collection unit can also analyze the accuracy of data collected by the user in the past and select the most accurate method. The collection unit can also suggest the most efficient collection timing based on the user's past data collection history. This enables efficient data collection by selecting the optimal method based on the past data collection history. Some or all of the above-mentioned processing in the collection unit can be performed using, for example, AI, or can be performed without using AI. For example, the collection unit can input the user's past data collection history into the generation AI and have the generation AI select the optimal collection method.

[0035] The collection unit can filter data based on the user's current health condition and lifestyle habits when collecting data. For example, if the user is in poor health, the collection unit temporarily suspends data collection. The collection unit can also collect detailed data when the user's health condition is good. The collection unit can also select the optimal timing for data collection based on the user's lifestyle habits (e.g., meal times, exercise time, etc.). This allows more appropriate data to be obtained by collecting data according to the user's health condition and lifestyle habits. Some or all of the above-mentioned processing in the collection unit may be performed using, or without, AI, for example. For example, the collection unit can input the user's health condition and lifestyle habit data into the generation AI and have the generation AI perform filtering.

[0036] When collecting data, the collection unit can select the optimal collection means depending on the user's input method. For example, if the user prefers voice input, the collection unit can prioritize voice input. Also, if the user prefers text input, the collection unit can prioritize text input. Also, if the user prefers image input, the collection unit can prioritize image input. This enables efficient data collection by selecting the optimal means depending on the user's input method. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input user input method data to a generation AI and have the generation AI select the optimal collection means.

[0037] When collecting data, the collection unit can prioritize collecting highly relevant data by taking into account the user's geographical location information. For example, when the user is at a gym, the collection unit prioritizes collecting data on exercise content. Furthermore, when the user is at a restaurant, the collection unit can prioritize collecting data on meal content. Furthermore, when the user is at home, the collection unit can prioritize collecting data on weight and body fat. This allows for efficient collection of highly relevant data by taking the user's geographical location information into account. Some or all of the above-described processing by the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the user's geographical location information to the generation AI and cause the generation AI to prioritize the collection of highly relevant data.

[0038] The collection unit can analyze the user's social media activity and collect related data when collecting data. For example, the collection unit automatically collects meal details shared by the user on social media. The collection unit can also automatically collect exercise details shared by the user on social media. The collection unit can also collect related health data from the user's social media activity. This allows for efficient collection of related data by analyzing social media activity. Some or all of the above-described processing in the collection unit can be performed using, for example, AI, or can be performed without using AI. For example, the collection unit can input the user's social media activity data into the generation AI and cause the generation AI to collect related data.

[0039] The collection unit can customize the collection method by reflecting the user's past feedback when collecting data. The collection unit selects the optimal data collection method based on, for example, feedback provided by the user in the past. The collection unit can also adjust the collection timing based on the user's past feedback. The collection unit can also customize the type of data to be collected by reflecting the user's past feedback. In this way, an optimal data collection method can be provided by reflecting the past feedback. Some or all of the above-described processing by the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the user's past feedback data into the generation AI and cause the generation AI to customize the collection 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 data with high importance (such as weight and body fat). The analysis unit can also perform a simplified analysis on data with low importance (such as dietary details and exercise details). The analysis unit can also dynamically adjust the level of detail of the analysis according to the importance of the data. This enables efficient analysis by performing analysis according to the importance of the 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 the importance of the data to the generation AI and cause the generation AI to adjust the level of detail of the analysis based on the importance.

[0041] During analysis, the analysis unit can apply different analysis algorithms depending on the data category. For example, the analysis unit applies a statistical analysis algorithm to weight and body fat data. The analysis unit can also apply an analysis algorithm that takes nutritional balance into consideration to dietary content data. The analysis unit can also apply an analysis algorithm that evaluates exercise effectiveness to exercise content data. This makes it possible to obtain more accurate analysis results by performing analysis according to the data category. 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 data category to the generation AI and cause the generation AI to apply an analysis algorithm according to the category.

[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, corrects the current analysis result based on the user's past analysis results. The analysis unit can also adjust the parameters of the analysis algorithm based on the user's past analysis results. The analysis unit can also dynamically improve the accuracy of the analysis 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 the generation AI and have the generation AI improve the accuracy of the analysis.

[0043] During analysis, the analysis unit can determine the analysis priority based on the time when the data was collected. For example, the analysis unit prioritizes the analysis of the most recent data. The analysis unit can also emphasize the most recent data while referring to past data. The analysis unit can also dynamically adjust the analysis priority according to the time when the data was collected. This enables analysis that emphasizes the most recent data by performing analysis based on the time when the data was collected. 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 when the data was collected into the generation AI and cause the generation AI to determine the analysis priority based on the time when the data was collected.

[0044] The analysis unit can adjust the order of analysis based on the relevance of data during analysis. For example, the analysis unit associates and analyzes data on weight and body fat. The analysis unit can also associate and analyze data on dietary content and exercise content. The analysis unit can also dynamically adjust the order of analysis according to the relevance of data. This enables efficient analysis by performing analysis based on the relevance of 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 relevance of data to the generation AI and cause the generation AI to adjust the order of analysis based on the relevance.

[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 use detailed technical terminology. Furthermore, if the user does not have technical expertise, the analysis unit can explain the analysis results in simple terms. Furthermore, the analysis unit can dynamically adjust the use of technical terminology in the analysis according to the user's level of expertise. This allows for the provision of analysis results that are easy to understand by providing analysis results that correspond to the user's level of expertise. Some or all of the above-described processing in the analysis unit may be performed using, or without, AI. For example, the analysis unit can input the user's level of expertise data into the generation AI and have the generation AI use technical terminology.

[0046] When making a suggestion, the suggestion unit can monitor the user's progress in real time and modify the suggestion content as necessary. For example, if the user is making steady progress toward the goal, the suggestion unit can suggest continuing the current plan. Also, if the user is falling behind toward the goal, the suggestion unit can suggest increasing the amount of exercise. The suggestion unit can also dynamically modify the meal plan and exercise plan according to the user's progress. This supports goal achievement by making flexible suggestions according to the user's progress. Some or all of the above-mentioned processing in the suggestion unit may be performed using, or without, AI, for example. For example, the suggestion unit can input the user's progress data into the generation AI and cause the generation AI to modify the suggestion content according to the progress.

[0047] When making a suggestion, the suggestion unit can analyze the user's past suggestion history and select the optimal suggestion method. For example, the suggestion unit analyzes the effects of suggestions the user has received in the past and selects the optimal suggestion method. The suggestion unit can also select the most effective suggestion method from the user's past suggestion history. The suggestion unit can also dynamically adjust the suggestion method by referring to the user's past suggestion history. This makes it possible to provide effective advice by selecting the optimal suggestion method based on the past suggestion history. Some or all of the above-mentioned processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can input the user's past suggestion history data into the generation AI and cause the generation AI to select the optimal suggestion method.

[0048] When making a proposal, the suggestion unit can customize the proposal content based on the user's current living situation and health condition. For example, if the user is busy, the suggestion unit can suggest a short and effective exercise plan. Furthermore, if the user is in poor health, the suggestion unit can also suggest a lighter exercise plan. Furthermore, the suggestion unit can dynamically customize meal plans and exercise plans according to the user's living situation and health condition. This allows for more appropriate advice to be provided by making suggestions based on the user's living situation and health condition. Some or all of the above-mentioned processing in the suggestion unit may be performed using, or without, AI, for example. For example, the suggestion unit can input the user's living situation and health condition data into the generation AI and have the generation AI customize the proposal content.

[0049] When making a suggestion, the suggestion unit can provide an optimal suggestion by taking into account the user's geographical location information. For example, if the user is at home, the suggestion unit can suggest an exercise plan that can be done at home. Furthermore, if the user is in a park, the suggestion unit can also suggest an exercise plan that can be done in the park. Furthermore, if the user is at a gym, the suggestion unit can also suggest an exercise plan using equipment available at the gym. In this way, optimal suggestions can be provided by taking into account the user's geographical location information. Some or all of the above-mentioned processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can input the user's geographical location information into the generation AI and cause the generation AI to provide optimal suggestions.

[0050] When making a suggestion, the suggestion unit can analyze the user's social media activity and make related suggestions. For example, the suggestion unit can suggest a related meal plan based on the meal details shared by the user on social media. The suggestion unit can also suggest a related exercise plan based on the exercise details shared by the user on social media. The suggestion unit can also make related health maintenance suggestions based on the user's social media activity. In this way, related suggestions can be made efficiently by analyzing social media activity. Some or all of the above-mentioned processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can input the user's social media activity data into a generation AI and cause the generation AI to generate related suggestions.

[0051] When making a proposal, the suggestion unit can customize the proposal method by reflecting the user's past feedback. The suggestion unit selects the optimal proposal method based on, for example, feedback provided by the user in the past. The suggestion unit can also adjust the proposal content based on the user's past feedback. The suggestion unit can also dynamically adjust the priority of proposals by reflecting the user's past feedback. In this way, the optimal proposal method can be provided by reflecting the past feedback. Some or all of the above-mentioned processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can input the user's past feedback data into the generation AI and cause the generation AI to customize the proposal method.

[0052] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0053] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the data. For example, a detailed analysis can be performed on data with high importance (such as weight and body fat). A simplified analysis can also be performed on data with low importance (such as dietary details and exercise details). Furthermore, the analysis unit can dynamically adjust the level of detail of the analysis according to the importance of the data. This enables efficient analysis by performing analysis according to the importance of the data. 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 importance of the data to the generation AI and cause the generation AI to adjust the level of detail of the analysis based on the importance.

[0054] When making a suggestion, the suggestion unit can monitor the user's progress in real time and modify the suggestion content as necessary. For example, if the user is making good progress toward the goal, the suggestion unit can suggest continuing the current plan. Also, if the user is falling behind toward the goal, the suggestion unit can suggest increasing the amount of exercise. Furthermore, the suggestion unit can dynamically modify the meal plan and exercise plan according to the user's progress. This supports goal achievement by making flexible suggestions according to the user's progress. Some or all of the above-mentioned processing in the suggestion unit may be performed using, or without, AI. For example, the suggestion unit can input the user's progress data into the generation AI and cause the generation AI to modify the suggestion content according to the progress.

[0055] The collection unit can analyze the user's past data collection history and select the optimal collection method. For example, it can prioritize and suggest data collection methods (such as manual input or voice input) that the user has frequently used in the past. The collection unit can also analyze the accuracy of data collected by the user in the past and select the most accurate method. Furthermore, the collection unit can also suggest the most efficient collection timing based on the user's past data collection history. This enables efficient data collection by selecting the optimal method based on the past data collection history. Some or all of the above-mentioned processing in the collection unit can be performed using, for example, AI, or can be performed without using AI. For example, the collection unit can input the user's past data collection history into the generation AI and have the generation AI select the optimal collection method.

[0056] During analysis, the analysis unit can apply different analysis algorithms depending on the data category. For example, a statistical analysis algorithm can be applied to weight and body fat data. The analysis unit can also apply an analysis algorithm that takes nutritional balance into account to dietary content data. Furthermore, the analysis unit can apply an analysis algorithm that evaluates exercise effectiveness to exercise content data. This allows for analysis according to the data category, thereby obtaining more accurate 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 data category into the generation AI and cause the generation AI to apply an analysis algorithm according to the category.

[0057] When making a suggestion, the suggestion unit can analyze the user's past suggestion history and select the optimal suggestion method. For example, the suggestion unit can analyze the effectiveness of suggestions the user has received in the past and select the optimal suggestion method. The suggestion unit can also select the most effective suggestion method from the user's past suggestion history. Furthermore, the suggestion unit can dynamically adjust the suggestion method by referring to the user's past suggestion history. This makes it possible to provide effective advice by selecting the optimal suggestion method based on the past suggestion history. Some or all of the above-mentioned processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can input the user's past suggestion history data into the generation AI and cause the generation AI to select the optimal suggestion method.

[0058] The collection unit can filter data based on the user's current health condition and lifestyle habits when collecting data. For example, if the user is in poor health, the collection unit can temporarily suspend data collection. The collection unit can also collect detailed data when the user's health condition is good. Furthermore, the collection unit can select the optimal timing for data collection based on the user's lifestyle habits (e.g., meal times, exercise time, etc.). This allows more appropriate data to be obtained by collecting data according to the user's health condition and lifestyle habits. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the user's health condition and lifestyle habit data into the generation AI and have the generation AI perform filtering.

[0059] The processing flow of the first embodiment will be briefly explained below.

[0060] Step 1: The collection unit collects data such as the user's diet and exercise that day, as well as weight, body fat, and bone age that can be obtained using a scale. For example, the collection unit records in detail what the user ate for breakfast, the calories consumed for lunch, and the contents of dinner. The collection unit can also record exercise details such as walking, running, and strength training, as well as the duration of the exercise. The collection unit also records data such as weight, body fat, and bone age that can be obtained using a scale. For example, the collection unit records the user's weight when they wake up in the morning, the body fat percentage after exercise, and bone age that is measured periodically. Step 2: The analysis unit analyzes the data collected by the collection unit. For example, the analysis unit suggests recommended dietary content and training methods based on the collected data for the target values ​​set by the user. The analysis unit can analyze the data using, for example, statistical analysis or machine learning algorithms. For example, if the user sets "I want to lose 3 kg in one month," the analysis unit will suggest specific dietary and exercise plans to achieve that goal. Step 3: The suggestion unit suggests recommended dietary and training methods based on the analysis results obtained by the analysis unit. For example, the suggestion unit provides specific advice such as, "Eat a nutritionally balanced meal for breakfast and choose a menu that includes lots of vegetables for lunch." The suggestion unit can also monitor the user's progress and modify the advice as needed. For example, if the user is making good progress toward their goal, the suggestion unit instructs them to continue with the plan as is, and conversely, if the user is falling behind, the suggestion unit suggests that they increase their exercise.

[0061] (Example 2) An AI personal trainer system according to an embodiment of the present invention collects and analyzes data such as a user's daily diet and exercise habits, as well as weight, body fat, and bone age, which can be obtained using a scale or other device, and proposes optimal dietary and training methods. The AI ​​personal trainer system collects and analyzes data such as a user's daily diet and exercise habits, as well as weight, body fat, and bone age, which can be obtained using a scale or other device, and proposes recommended dietary and training methods for the user's set goals. For example, the AI ​​personal trainer system records the user's daily dietary and exercise habits. For example, the system records details such as what the user ate for breakfast, the calories consumed for lunch, and the contents of dinner. The system also records the type and duration of exercise, such as walking, running, or strength training. This information is submitted to the AI ​​personal trainer. The AI ​​personal trainer system then records data such as the user's weight, body fat, and bone age, which can be obtained using a scale or other device. For example, the user's weight upon waking up, their body fat percentage after exercise, and their bone age, which are measured periodically. This data is also submitted to the AI ​​personal trainer. The AI ​​personal trainer system then analyzes the submitted data and proposes recommended dietary and training methods for the user's set goals. For example, if a user sets a goal of "I want to lose 3 kg in one month," the AI ​​will propose specific meal and exercise plans toward that goal. For example, it may provide advice such as, "Eat a nutritionally balanced meal for breakfast and choose a menu rich in vegetables for lunch." Furthermore, the AI ​​personal trainer system monitors the user's progress and adjusts its advice as necessary. For example, if the user is making steady progress toward their goal, it will instruct them to continue with the plan as is. Conversely, if they are falling behind, it will suggest increasing the amount of exercise. In this way, the user can always receive optimal advice. This allows the AI ​​personal trainer system to easily maintain health at home, without having to go to the gym. This effectively supports users in maintaining their health.For example, even people with limited time, such as busy businessmen or housewives raising children, can manage their health at their own pace. In addition, the advice provided by AI is optimized for each individual user, so effective dieting and health maintenance can be expected.

[0062] The AI ​​personal trainer system according to the embodiment includes a collection unit, an analysis unit, and a suggestion unit. The collection unit collects data such as the user's daily diet and exercise habits, as well as weight, body fat, and bone age, which can be obtained using a scale or other device. For example, the collection unit records in detail what the user ate for breakfast, the calories consumed for lunch, and the contents of dinner. The collection unit can also record exercise habits such as walking, running, and strength training, along with the duration of the exercise. The collection unit also records data such as weight, body fat, and bone age, which can be obtained using a scale. For example, the collection unit records the user's weight upon waking up in the morning, the body fat percentage after exercise, and bone age, which is measured periodically. The analysis unit analyzes the data collected by the collection unit. For example, the analysis unit recommends dietary and training methods based on the collected data for a target value set by the user. The analysis unit can analyze the data using, for example, statistical analysis or a machine learning algorithm. For example, if a user sets a goal of "losing 3 kg in one month," the analysis unit can suggest a specific diet plan and exercise plan to achieve that goal. The suggestion unit recommends dietary and training methods based on the analysis results obtained by the analysis unit. For example, the suggestion unit provides specific advice such as, "Eat a nutritionally balanced meal for breakfast and choose a menu rich in vegetables for lunch." The suggestion unit can also monitor the user's progress and modify the advice as needed. For example, if the user is making steady progress toward their goal, the suggestion unit instructs the user to continue with the plan as is, while if the user is falling behind, the suggestion unit suggests increasing the amount of exercise. This allows the AI ​​personal trainer system according to the embodiment to effectively support users in maintaining their health. For example, even people with limited time, such as busy businessmen or housewives raising children, can manage their health at their own pace. Furthermore, the advice provided by the AI ​​is optimized for each individual user, which can lead to effective dieting and health maintenance.

[0063] The collection unit can collect data on the user's diet or exercise intake for the day, weight or body fat obtained using a scale, and bone age. For example, the collection unit can record in detail what the user ate for breakfast, the calories consumed for lunch, and the contents of dinner. The collection unit can also record exercise details and duration, such as walking, running, and strength training. Furthermore, the collection unit records data on weight, body fat, bone age, and other data obtained using a scale. For example, the collection unit can record the user's weight upon waking up, the body fat percentage after exercise, and bone age measured periodically. By collecting detailed health data on the user, more accurate analysis and recommendations are possible. Some or all of the above-described processing by the collection unit may be performed using, or without, AI. For example, the collection unit can input the user's diet and exercise data into the generation AI and have the generation AI organize and complete the data.

[0064] The analysis unit can analyze the data collected by the collection unit and suggest recommended dietary content or training methods for target numerical values ​​set by the user. For example, the analysis unit can suggest recommended dietary content or training methods based on the collected data for target numerical values ​​set by the user. The analysis unit can analyze the data using, for example, statistical analysis or machine learning algorithms. For example, if a user sets a goal of "losing 3 kilograms in one month," the analysis unit can suggest specific dietary and exercise plans toward that goal. This makes it possible to effectively maintain health by making specific suggestions based on the user's goals. Some or all of the above-mentioned processing by the analysis unit can be performed, for example, using AI or without AI. For example, the analysis unit can input the collected data into a generation AI and have the generation AI analyze the data and generate suggestions.

[0065] The suggestion unit can monitor the user's progress and change the advice as necessary. For example, the suggestion unit monitors the user's progress and modifies the advice as necessary. For example, if the user is making good progress toward the goal, the suggestion unit instructs the user to continue with the plan as is, and conversely, if the user is falling behind the goal, the suggestion unit suggests increasing the amount of exercise. This supports goal achievement by providing flexible advice according to the user's progress. Some or all of the above-mentioned processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can input the user's progress data into the generation AI and cause the generation AI to modify the advice according to the progress.

[0066] The suggestion unit can propose a specific meal plan or exercise plan based on the target values ​​set by the user. For example, the suggestion unit may provide specific advice such as, "Eat a nutritionally balanced meal for breakfast and choose a menu rich in vegetables for lunch." This allows for effective health maintenance by providing a specific plan tailored to the user's goals. Some or all of the above-described processing by the suggestion unit may be performed using, or without, AI. For example, the suggestion unit may input the user's target values ​​and collected data into the generation AI and cause the generation AI to generate a specific meal plan or exercise plan.

[0067] The collection unit can estimate the user's emotions and adjust the timing of data collection based on the estimated user emotions. For example, if the user is feeling stressed, the collection unit adjusts data collection to occur during a relaxed time period. Furthermore, if the user is relaxed, the collection unit can also adjust data collection to occur immediately after a meal or exercise. Furthermore, if the user is busy, the collection unit can also adjust data collection to occur during a less busy time period. This allows data to be collected at the optimal timing according to the user's emotions, thereby obtaining more accurate data. The emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. Examples of the generation AI include, but are not limited to, text generation AI (e.g., LLM) and multimodal generation AI. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or without AI. For example, the collection unit can input the user's emotion data into the generation AI and cause the generation AI to adjust the timing of data collection based on the emotion.

[0068] The collection unit can analyze the user's past data collection history and select the optimal collection method. For example, the collection unit can prioritize and suggest data collection methods (such as manual input or voice input) that the user has frequently used in the past. The collection unit can also analyze the accuracy of data collected by the user in the past and select the most accurate method. The collection unit can also suggest the most efficient collection timing based on the user's past data collection history. This enables efficient data collection by selecting the optimal method based on the past data collection history. Some or all of the above-mentioned processing in the collection unit can be performed using, for example, AI, or can be performed without using AI. For example, the collection unit can input the user's past data collection history into the generation AI and have the generation AI select the optimal collection method.

[0069] The collection unit can filter data based on the user's current health condition and lifestyle habits when collecting data. For example, if the user is in poor health, the collection unit temporarily suspends data collection. The collection unit can also collect detailed data when the user's health condition is good. The collection unit can also select the optimal timing for data collection based on the user's lifestyle habits (e.g., meal times, exercise time, etc.). This allows more appropriate data to be obtained by collecting data according to the user's health condition and lifestyle habits. Some or all of the above-mentioned processing in the collection unit may be performed using, or without, AI, for example. For example, the collection unit can input the user's health condition and lifestyle habit data into the generation AI and have the generation AI perform filtering.

[0070] When collecting data, the collection unit can select the optimal collection means depending on the user's input method. For example, if the user prefers voice input, the collection unit can prioritize voice input. Also, if the user prefers text input, the collection unit can prioritize text input. Also, if the user prefers image input, the collection unit can prioritize image input. This enables efficient data collection by selecting the optimal means depending on the user's input method. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input user input method data to a generation AI and have the generation AI select the optimal collection means.

[0071] The collection unit can estimate the user's emotions and determine the priority of data to be collected based on the estimated user emotions. For example, when the user is feeling stressed, the collection unit can prioritize collecting data on weight and body fat. Furthermore, when the user is relaxed, the collection unit can also prioritize collecting data on dietary and exercise details. Furthermore, when the user is busy, the collection unit can prioritize collecting only the most important data. Thus, by prioritizing data according to the user's emotions, important data can be collected preferentially. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. Examples of the generation AI include, but are not limited to, text generation AI (e.g., LLM) and multimodal generation AI. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or without AI. For example, the collection unit can input the user's emotion data into the generation AI and have the generation AI determine the priority of data based on emotions.

[0072] When collecting data, the collection unit can prioritize collecting highly relevant data by taking into account the user's geographical location information. For example, when the user is at a gym, the collection unit prioritizes collecting data on exercise content. Furthermore, when the user is at a restaurant, the collection unit can prioritize collecting data on meal content. Furthermore, when the user is at home, the collection unit can prioritize collecting data on weight and body fat. This allows for efficient collection of highly relevant data by taking the user's geographical location information into account. Some or all of the above-described processing by the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the user's geographical location information to the generation AI and cause the generation AI to prioritize the collection of highly relevant data.

[0073] The collection unit can analyze the user's social media activity and collect related data when collecting data. For example, the collection unit automatically collects meal details shared by the user on social media. The collection unit can also automatically collect exercise details shared by the user on social media. The collection unit can also collect related health data from the user's social media activity. This allows for efficient collection of related data by analyzing social media activity. Some or all of the above-described processing in the collection unit can be performed using, for example, AI, or can be performed without using AI. For example, the collection unit can input the user's social media activity data into the generation AI and cause the generation AI to collect related data.

[0074] The collection unit can customize the collection method by reflecting the user's past feedback when collecting data. The collection unit selects the optimal data collection method based on, for example, feedback provided by the user in the past. The collection unit can also adjust the collection timing based on the user's past feedback. The collection unit can also customize the type of data to be collected by reflecting the user's past feedback. In this way, an optimal data collection method can be provided by reflecting the past feedback. Some or all of the above-described processing by the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the user's past feedback data into the generation AI and cause the generation AI to customize the collection method.

[0075] The analysis unit can estimate the user's emotions and adjust the presentation method of the analysis based on the estimated user's emotions. For example, if the user is feeling stressed, the analysis unit can provide a simple and visually easy-to-understand analysis result. Furthermore, if the user is relaxed, the analysis unit can provide a detailed analysis result. Furthermore, if the user is excited, the analysis unit can provide an analysis result with a visually stimulating effect. This allows for analysis results that correspond to the user's emotions to be easily understood. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. Examples of the generation AI include, but are not limited to, text generation AI (e.g., LLM) and multimodal generation AI. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or without AI. For example, the analysis unit can input the user's emotion data into the generation AI and cause the generation AI to adjust the presentation method of the analysis based on the emotion.

[0076] 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 data with high importance (such as weight and body fat). The analysis unit can also perform a simplified analysis on data with low importance (such as dietary details and exercise details). The analysis unit can also dynamically adjust the level of detail of the analysis according to the importance of the data. This enables efficient analysis by performing analysis according to the importance of the 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 the importance of the data to the generation AI and cause the generation AI to adjust the level of detail of the analysis based on the importance.

[0077] During analysis, the analysis unit can apply different analysis algorithms depending on the data category. For example, the analysis unit applies a statistical analysis algorithm to weight and body fat data. The analysis unit can also apply an analysis algorithm that takes nutritional balance into consideration to dietary content data. The analysis unit can also apply an analysis algorithm that evaluates exercise effectiveness to exercise content data. This makes it possible to obtain more accurate analysis results by performing analysis according to the data category. 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 data category to the generation AI and cause the generation AI to apply an analysis algorithm according to the category.

[0078] 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, corrects the current analysis result based on the user's past analysis results. The analysis unit can also adjust the parameters of the analysis algorithm based on the user's past analysis results. The analysis unit can also dynamically improve the accuracy of the analysis 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 the generation AI and have the generation AI improve the accuracy of the analysis.

[0079] The analysis unit can estimate the user's emotions and adjust the length of the analysis based on the estimated user emotions. For example, if the user is in a hurry, the analysis unit can provide a short and concise analysis result. If the user is relaxed, the analysis unit can also provide a detailed analysis result. If the user is excited, the analysis unit can also provide an analysis result with a visually stimulating effect. This allows for appropriate analysis results to be provided by adjusting the length of the analysis according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. Examples of the generation AI include, but are not limited to, text generation AI (e.g., LLM) and multimodal generation AI. Some or all of the above-described processing in the analysis unit may be performed using AI, or may be performed without AI. For example, the analysis unit can input the user's emotion data into the generation AI and cause the generation AI to adjust the length of the analysis based on the emotion.

[0080] During analysis, the analysis unit can determine the analysis priority based on the time when the data was collected. For example, the analysis unit prioritizes the analysis of the most recent data. The analysis unit can also emphasize the most recent data while referring to past data. The analysis unit can also dynamically adjust the analysis priority according to the time when the data was collected. This enables analysis that emphasizes the most recent data by performing analysis based on the time when the data was collected. 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 when the data was collected into the generation AI and cause the generation AI to determine the analysis priority based on the time when the data was collected.

[0081] The analysis unit can adjust the order of analysis based on the relevance of data during analysis. For example, the analysis unit associates and analyzes data on weight and body fat. The analysis unit can also associate and analyze data on dietary content and exercise content. The analysis unit can also dynamically adjust the order of analysis according to the relevance of data. This enables efficient analysis by performing analysis based on the relevance of 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 relevance of data to the generation AI and cause the generation AI to adjust the order of analysis based on the relevance.

[0082] 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 use detailed technical terminology. Furthermore, if the user does not have technical expertise, the analysis unit can explain the analysis results in simple terms. Furthermore, the analysis unit can dynamically adjust the use of technical terminology in the analysis according to the user's level of expertise. This allows for the provision of analysis results that are easy to understand by providing analysis results that correspond to the user's level of expertise. Some or all of the above-described processing in the analysis unit may be performed using, or without, AI. For example, the analysis unit can input the user's level of expertise data into the generation AI and have the generation AI use technical terminology.

[0083] The suggestion unit can estimate the user's emotions and adjust the way suggestions are expressed based on the estimated user emotions. For example, if the user is feeling stressed, the suggestion unit can make simple, visually easy-to-understand suggestions. Furthermore, if the user is relaxed, the suggestion unit can make detailed suggestions. Furthermore, if the user is excited, the suggestion unit can make suggestions with visually stimulating effects. This allows for more effective advice by making suggestions based on the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. Examples of the generation AI include, but are not limited to, text generation AI (e.g., LLM) and multimodal generation AI. Some or all of the above-described processing in the suggestion unit may be performed using, for example, AI, or without AI. For example, the suggestion unit can input the user's emotion data into the generation AI and cause the generation AI to adjust the way suggestions are expressed based on the emotion.

[0084] When making a suggestion, the suggestion unit can monitor the user's progress in real time and modify the suggestion content as necessary. For example, if the user is making steady progress toward the goal, the suggestion unit can suggest continuing the current plan. Also, if the user is falling behind toward the goal, the suggestion unit can suggest increasing the amount of exercise. The suggestion unit can also dynamically modify the meal plan and exercise plan according to the user's progress. This supports goal achievement by making flexible suggestions according to the user's progress. Some or all of the above-mentioned processing in the suggestion unit may be performed using, or without, AI, for example. For example, the suggestion unit can input the user's progress data into the generation AI and cause the generation AI to modify the suggestion content according to the progress.

[0085] When making a suggestion, the suggestion unit can analyze the user's past suggestion history and select the optimal suggestion method. For example, the suggestion unit analyzes the effects of suggestions the user has received in the past and selects the optimal suggestion method. The suggestion unit can also select the most effective suggestion method from the user's past suggestion history. The suggestion unit can also dynamically adjust the suggestion method by referring to the user's past suggestion history. This makes it possible to provide effective advice by selecting the optimal suggestion method based on the past suggestion history. Some or all of the above-mentioned processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can input the user's past suggestion history data into the generation AI and cause the generation AI to select the optimal suggestion method.

[0086] When making a proposal, the suggestion unit can customize the proposal content based on the user's current living situation and health condition. For example, if the user is busy, the suggestion unit can suggest a short and effective exercise plan. Furthermore, if the user is in poor health, the suggestion unit can also suggest a lighter exercise plan. Furthermore, the suggestion unit can dynamically customize meal plans and exercise plans according to the user's living situation and health condition. This allows for more appropriate advice to be provided by making suggestions based on the user's living situation and health condition. Some or all of the above-mentioned processing in the suggestion unit may be performed using, or without, AI, for example. For example, the suggestion unit can input the user's living situation and health condition data into the generation AI and have the generation AI customize the proposal content.

[0087] The suggestion unit can estimate the user's emotions and prioritize suggestions based on the estimated user emotions. For example, if the user is feeling stressed, the suggestion unit can prioritize suggesting meal plans with a relaxing effect. Furthermore, if the user is relaxed, the suggestion unit can prioritize suggesting exercise plans. Furthermore, if the user is excited, the suggestion unit can prioritize suggesting exercise plans with high energy consumption. Thus, by prioritizing suggestions according to the user's emotions, important suggestions can be prioritized. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. Examples of the generation AI include, but are not limited to, text generation AI (e.g., LLM) and multimodal generation AI. Some or all of the above-described processing in the suggestion unit may be performed using, for example, AI, or without AI. For example, the suggestion unit can input the user's emotion data into the generation AI and cause the generation AI to prioritize suggestions based on emotions.

[0088] When making a suggestion, the suggestion unit can provide an optimal suggestion by taking into account the user's geographical location information. For example, if the user is at home, the suggestion unit can suggest an exercise plan that can be done at home. Furthermore, if the user is in a park, the suggestion unit can also suggest an exercise plan that can be done in the park. Furthermore, if the user is at a gym, the suggestion unit can also suggest an exercise plan using equipment available at the gym. In this way, optimal suggestions can be provided by taking into account the user's geographical location information. Some or all of the above-mentioned processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can input the user's geographical location information into the generation AI and cause the generation AI to provide optimal suggestions.

[0089] When making a suggestion, the suggestion unit can analyze the user's social media activity and make related suggestions. For example, the suggestion unit can suggest a related meal plan based on the meal details shared by the user on social media. The suggestion unit can also suggest a related exercise plan based on the exercise details shared by the user on social media. The suggestion unit can also make related health maintenance suggestions based on the user's social media activity. In this way, related suggestions can be made efficiently by analyzing social media activity. Some or all of the above-mentioned processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can input the user's social media activity data into a generation AI and cause the generation AI to generate related suggestions.

[0090] When making a proposal, the suggestion unit can customize the proposal method by reflecting the user's past feedback. The suggestion unit selects the optimal proposal method based on, for example, feedback provided by the user in the past. The suggestion unit can also adjust the proposal content based on the user's past feedback. The suggestion unit can also dynamically adjust the priority of proposals by reflecting the user's past feedback. In this way, the optimal proposal method can be provided by reflecting the past feedback. Some or all of the above-mentioned processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can input the user's past feedback data into the generation AI and cause the generation AI to customize the proposal method. === Hard Collateral 1-1 === Each of the multiple elements including the collection unit, analysis unit, and suggestion 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 collection unit records the user's diet and exercise using the camera 42 and microphone 38B of the smart device 14 and collects data from a weight scale, etc. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, analyzes the collected data, and suggests optimal diet and training methods for the user's goals. The suggestion unit is realized, for example, by the control unit 46A of the smart device 14, and provides specific advice to the user based on the analysis results. === Hard Collateral 1-2 === Each of the multiple elements, including the collection unit, analysis unit, and suggestion unit, described above, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the collection unit uses the camera 42 and microphone 238 of the smart glasses 214 to record the user's diet and exercise, and collect data such as a weight scale. The analysis unit, realized, for example, by the specific processing unit 290 of the data processing device 12, analyzes the collected data, and suggests optimal diet and training methods for the user's goals. The suggestion unit, realized, for example, by the control unit 46A of the smart glasses 214, provides specific advice to the user based on the analysis results. === Hard Collateral 1-3 === Each of the multiple elements including the collection unit, analysis unit, and suggestion unit described above is realized, for example, by at least one of the headset-type terminal 314 and the data processing device 12. For example, the collection unit records the user's diet and exercise using the camera 42 and microphone 238 of the headset-type terminal 314 and collects data from a weight scale, etc. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, analyzes the collected data, and suggests optimal diet and training methods for the user's goals. The suggestion unit is realized, for example, by the control unit 46A of the headset-type terminal 314, and provides specific advice to the user based on the analysis results. === Hard Collateral 1-4 === Each of the multiple elements including the collection unit, analysis unit, and suggestion unit described above is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the collection unit uses the camera 42 and microphone 238 of the robot 414 to record the user's diet and exercise, and collects data from a weight scale, etc. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, analyzes the collected data, and suggests optimal dietary content and training methods for the user's goals. The suggestion unit is realized, for example, by the control unit 46A of the robot 414, and provides specific advice to the user based on the analysis results.

[0091] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0092] The analysis unit can estimate the user's emotions and determine analysis priorities based on the estimated user emotions. For example, if the user is feeling stressed, data related to stress reduction (e.g., relaxing diet and exercise) can be prioritized for analysis. Furthermore, if the user is relaxed, data related to long-term health maintenance (e.g., nutritional balance and sustainable exercise plans) can be prioritized for analysis. Furthermore, if the user is excited, data related to high-energy exercise and high-calorie diets can be prioritized for analysis. This enables more effective health management by determining analysis priorities based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. Examples of generative AI include, but are not limited to, text generation AI (e.g., LLM) and multimodal generation AI. Some or all of the above-described processing in the analysis unit may be performed using AI, or without AI. For example, the analysis unit can input the user's emotion data into the generative AI and have the generative AI determine the analysis priorities based on emotions.

[0093] The suggestion unit can estimate the user's emotions and adjust the way suggestions are presented based on the estimated user emotions. For example, if the user is stressed, the suggestion unit can provide simple, visually easy-to-understand suggestions. If the user is relaxed, the suggestion unit can provide detailed suggestions. If the user is excited, the suggestion unit can provide suggestions with visually stimulating effects. This allows for more effective advice by providing suggestions tailored to the user's emotions. The emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. Examples of the generation AI include, but are not limited to, text generation AI (e.g., LLM) and multimodal generation AI. Some or all of the above-described processing in the suggestion unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the suggestion unit can input the user's emotion data into the generation AI and cause the generation AI to adjust the way suggestions are presented based on the emotion.

[0094] The collection unit can estimate the user's emotions and adjust the timing of data collection based on the estimated user emotions. For example, if the user is feeling stressed, the collection unit can adjust data collection to occur during a relaxed time period. Furthermore, if the user is relaxed, the collection unit can also adjust data collection to occur immediately after a meal or exercise. Furthermore, if the user is busy, the collection unit can also adjust data collection to occur during a less busy time period. This allows data to be collected at the optimal timing according to the user's emotions, thereby obtaining more accurate data. The emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. Examples of the generation AI include, but are not limited to, text generation AI (e.g., LLM) and multimodal generation AI. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or without AI. For example, the collection unit can input the user's emotion data into the generation AI and cause the generation AI to adjust the timing of data collection based on the emotion.

[0095] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the data. For example, a detailed analysis can be performed on data with high importance (such as weight and body fat). A simplified analysis can also be performed on data with low importance (such as dietary details and exercise details). Furthermore, the analysis unit can dynamically adjust the level of detail of the analysis according to the importance of the data. This enables efficient analysis by performing analysis according to the importance of the data. 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 importance of the data to the generation AI and cause the generation AI to adjust the level of detail of the analysis based on the importance.

[0096] When making a suggestion, the suggestion unit can monitor the user's progress in real time and modify the suggestion content as necessary. For example, if the user is making good progress toward the goal, the suggestion unit can suggest continuing the current plan. Also, if the user is falling behind toward the goal, the suggestion unit can suggest increasing the amount of exercise. Furthermore, the suggestion unit can dynamically modify the meal plan and exercise plan according to the user's progress. This supports goal achievement by making flexible suggestions according to the user's progress. Some or all of the above-mentioned processing in the suggestion unit may be performed using, or without, AI. For example, the suggestion unit can input the user's progress data into the generation AI and cause the generation AI to modify the suggestion content according to the progress.

[0097] The collection unit can analyze the user's past data collection history and select the optimal collection method. For example, it can prioritize and suggest data collection methods (such as manual input or voice input) that the user has frequently used in the past. The collection unit can also analyze the accuracy of data collected by the user in the past and select the most accurate method. Furthermore, the collection unit can also suggest the most efficient collection timing based on the user's past data collection history. This enables efficient data collection by selecting the optimal method based on the past data collection history. Some or all of the above-mentioned processing in the collection unit can be performed using, for example, AI, or can be performed without using AI. For example, the collection unit can input the user's past data collection history into the generation AI and have the generation AI select the optimal collection method.

[0098] During analysis, the analysis unit can apply different analysis algorithms depending on the data category. For example, a statistical analysis algorithm can be applied to weight and body fat data. The analysis unit can also apply an analysis algorithm that takes nutritional balance into account to dietary content data. Furthermore, the analysis unit can apply an analysis algorithm that evaluates exercise effectiveness to exercise content data. This allows for analysis according to the data category, thereby obtaining more accurate 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 data category into the generation AI and cause the generation AI to apply an analysis algorithm according to the category.

[0099] When making a suggestion, the suggestion unit can analyze the user's past suggestion history and select the optimal suggestion method. For example, the suggestion unit can analyze the effectiveness of suggestions the user has received in the past and select the optimal suggestion method. The suggestion unit can also select the most effective suggestion method from the user's past suggestion history. Furthermore, the suggestion unit can dynamically adjust the suggestion method by referring to the user's past suggestion history. This makes it possible to provide effective advice by selecting the optimal suggestion method based on the past suggestion history. Some or all of the above-mentioned processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can input the user's past suggestion history data into the generation AI and cause the generation AI to select the optimal suggestion method.

[0100] The collection unit can filter data based on the user's current health condition and lifestyle habits when collecting data. For example, if the user is in poor health, the collection unit can temporarily suspend data collection. The collection unit can also collect detailed data when the user's health condition is good. Furthermore, the collection unit can select the optimal timing for data collection based on the user's lifestyle habits (e.g., meal times, exercise time, etc.). This allows more appropriate data to be obtained by collecting data according to the user's health condition and lifestyle habits. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input the user's health condition and lifestyle habit data into the generation AI and have the generation AI perform filtering.

[0101] The suggestion unit can estimate the user's emotions and prioritize suggestions based on the estimated user emotions. For example, if the user is feeling stressed, it can prioritize suggesting meal plans with a relaxing effect. Also, if the user is relaxed, it can prioritize suggesting exercise plans. Furthermore, if the user is excited, it can prioritize suggesting exercise plans with high energy consumption. By prioritizing suggestions based on the user's emotions, it is possible to prioritize important suggestions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. Examples of the generation AI include, but are not limited to, text generation AI (e.g., LLM) and multimodal generation AI. Some or all of the above-described processing in the suggestion unit may be performed using AI, or may be performed without AI. For example, the suggestion unit can input the user's emotion data into the generation AI and cause the generation AI to prioritize suggestions based on emotions.

[0102] The processing flow of the second embodiment will be briefly explained below.

[0103] Step 1: The collection unit collects data such as the user's diet and exercise that day, as well as weight, body fat, and bone age that can be obtained using a scale. For example, the collection unit records in detail what the user ate for breakfast, the calories consumed for lunch, and the contents of dinner. The collection unit can also record exercise details such as walking, running, and strength training, as well as the duration of the exercise. The collection unit also records data such as weight, body fat, and bone age that can be obtained using a scale. For example, the collection unit records the user's weight when they wake up in the morning, the body fat percentage after exercise, and bone age that is measured periodically. Step 2: The analysis unit analyzes the data collected by the collection unit. For example, the analysis unit suggests recommended dietary content and training methods based on the collected data for the target values ​​set by the user. The analysis unit can analyze the data using, for example, statistical analysis or machine learning algorithms. For example, if the user sets "I want to lose 3 kg in one month," the analysis unit will suggest specific dietary and exercise plans to achieve that goal. Step 3: The suggestion unit suggests recommended dietary and training methods based on the analysis results obtained by the analysis unit. For example, the suggestion unit provides specific advice such as, "Eat a nutritionally balanced meal for breakfast and choose a menu that includes lots of vegetables for lunch." The suggestion unit can also monitor the user's progress and modify the advice as needed. For example, if the user is making good progress toward their goal, the suggestion unit instructs them to continue with the plan as is, and conversely, if the user is falling behind, the suggestion unit suggests that they increase their exercise.

[0104] 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.

[0105] 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.

[0106] 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.

[0107] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0108] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] 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.

[0113] 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).

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0124] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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).

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0140] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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).

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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).

[0161] 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.

[0162] 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."

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] [Explanation of symbols]

[0176] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a collection unit that collects data; an analysis unit that analyzes the data collected by the collection unit; a suggestion unit that suggests recommended meal contents or training methods based on the analysis results obtained by the analysis unit; Equipped with A system characterized by:

2. The collecting unit Collect data on the user's diet or exercise on that day, weight or body fat obtained by a scale, and bone age.

2. The system of claim 1.

3. The analysis unit The data collected by the collection unit is analyzed, and a recommended dietary content or training method is proposed for the target numerical value set by the user.

2. The system of claim 1.

4. The proposal unit Monitor user progress and change advice as needed 2. The system of claim 1.

5. The proposal unit Suggest specific meal or exercise plans based on the goals you set 2. The system of claim 1.

6. The collecting unit Estimate user emotions and adjust data collection timing based on the estimated user emotions 2. The system of claim 1.

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

8. The collecting unit When collecting data, filtering is performed based on the user's current health status and lifestyle habits.

2. The system of claim 1.

9. The collecting unit When collecting data, select the optimal collection method depending on the user's input method 2. The system of claim 1.

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