System

Abstract

Systems according to embodiments are directed to providing customized guidance based on an individual's behavior patterns and habits.SOLUTION: A system according to an embodiment includes a data collection unit, an analysis unit, and an advice providing unit. The data collection unit collects a behavior pattern, a habit, and a living situation of an individual. The analyzer analyzes the data collected by the data collector and generates information for providing customized guidance tailored to individual goals and needs. The advice providing unit provides appropriate advice or guidance based on the information generated by the analyzing 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 technologies have difficulty providing customized guidance based on an individual's behavioral patterns and habits, and there is room for improvement.

[0005] The system according to the embodiment aims to provide customized instruction based on an individual's behavioral patterns and habits. [Means for solving the problem]

[0006] The system according to the embodiment includes a data collection unit, an analysis unit, and an advice provision unit. The data collection unit collects an individual's behavioral patterns, habits, and living situations. The analysis unit analyzes the data collected by the data collection unit and generates information for providing customized guidance tailored to individual goals and needs. The advice provision unit provides appropriate advice and guidance based on the information generated by the analysis unit. [Effects of the Invention]

[0007] An embodiment of the system can provide customized instruction based on an individual's behavioral patterns and habits. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) A customized instruction system according to an embodiment of the present invention collects and analyzes an individual's behavioral patterns, habits, and lifestyle circumstances, and provides customized instruction tailored to the individual's goals and needs. The customized instruction system collects and analyzes an individual's behavioral patterns, habits, and lifestyle circumstances, generates information for providing customized instruction tailored to the individual's goals and needs, and provides optimal advice and guidance. The customized instruction system also works with IoT devices and wearable devices to automatically collect logs, manage health status, perform various analyses, and provide improvement advice. For example, the customized instruction system collects an individual's behavioral patterns, habits, and lifestyle circumstances. For example, the customized instruction system uses a wearable device to collect data such as heart rate, step count, and sleep patterns. The customized instruction system then analyzes the collected data. AI analyzes the collected data and generates information for providing customized instruction tailored to the individual's goals and needs. For example, the AI ​​analyzes an individual's exercise habits and eating patterns and proposes optimal exercise and meal plans. The customized instruction system then provides optimal advice and guidance based on the generated information. For example, the customized instruction system provides exercise and dietary advice tailored to the individual's goals. The customized guidance system also works with IoT and wearable devices to automatically collect logs, manage health status, perform various analyses, and provide advice on improvement. For example, the customized guidance system uses a wearable device to monitor heart rate and sleep patterns in real time to understand health status. This allows the customized guidance system to understand an individual's health status in real time and provide appropriate advice. This allows the customized guidance system to provide customized guidance based on an individual's behavioral patterns, habits, and lifestyle. For example, it can understand an individual's health status in real time and provide appropriate advice. It can also promote personal growth by providing exercise and dietary advice tailored to an individual's goals.

[0029] A customized instruction system according to an embodiment includes a data collection unit, an analysis unit, and an advice provision unit. The data collection unit collects an individual's behavioral patterns, habits, and lifestyle. Examples of the individual's behavioral patterns, habits, and lifestyle include, but are not limited to, daily movement patterns, exercise habits, eating habits, sleep habits, home environment, and work environment. The data collection unit collects data such as heart rate, step count, and sleep patterns using a wearable device. The data collection unit can also collect the individual's behavioral patterns, habits, and lifestyle using a smartphone or IoT device. For example, the data collection unit collects daily movement patterns using the GPS function of a smartphone. The data collection unit can also collect data on home and work environments using an IoT device. The analysis unit analyzes the collected data and generates information for providing customized instruction tailored to individual goals and needs. The analysis can be performed using, for example, AI, but is not limited to, an example. For example, the analysis unit can analyze an individual's exercise habits and eating patterns using AI to propose optimal exercise and meal plans. The analysis unit can also use AI to analyze an individual's sleep patterns and propose an optimal sleep plan. The analysis unit can also use AI to analyze an individual's stress level and provide advice for stress reduction. For example, the analysis unit can use AI to analyze data such as heart rate, step count, and sleep patterns, and generate information for providing customized guidance tailored to individual goals and needs. The advice providing unit provides optimal advice and guidance based on the generated information. The advice includes, but is not limited to, exercise and diet advice tailored to the individual's goals. For example, the advice providing unit can provide an exercise plan tailored to the individual's goals. The advice providing unit can also provide a meal plan tailored to the individual's goals. The advice providing unit can also provide a sleep plan tailored to the individual's goals. For example, the advice providing unit can provide exercise and diet advice tailored to the individual's goals. As a result, the customized guidance system according to the embodiment can provide customized guidance based on an individual's behavioral patterns, habits, and lifestyle.Some or all of the above-described processing in the advice providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the advice providing unit may provide optimal advice or guidance based on information generated using AI.

[0030] The customized guidance system includes a resource management unit that works in conjunction with IoT devices and wearable devices to automatically collect logs, manage health status, perform multiple analyses, and provide improvement advice. The resource management unit works in conjunction with IoT devices and wearable devices to automatically collect logs. The logs include, but are not limited to, heart rate, step count, and sleep data. The resource management unit collects heart rate, step count, and sleep data using, for example, a wearable device. The resource management unit can also collect data on home and work environments using IoT devices. For example, the resource management unit collects home environment data using smart home devices. The resource management unit can also collect work environment data using smart office devices. The resource management unit manages health status and performs multiple analyses based on the collected logs. The analysis is performed using, for example, AI, but is not limited to, an example. For example, the resource management unit uses AI to analyze heart rate, step count, and sleep data to manage health status. The resource management unit can also use AI to analyze home and work environment data to manage health status. The resource management unit provides improvement advice based on the collected logs. The advice includes, but is not limited to, advice on improving exercise and diet. For example, the resource management unit may analyze the collected data using AI and propose optimal exercise and meal plans. The resource management unit may also analyze the collected data using AI and provide advice on stress reduction. This enables health status management and improvement advice by linking with IoT and wearable devices. Some or all of the above-described processing in the resource management unit may be performed using AI, for example, or may be performed without using AI. For example, the resource management unit may select an optimal management method based on data collected using AI.

[0031] The data collection unit can collect data such as heart rate, number of steps, and sleep patterns using a wearable device. The data collection unit collects data such as heart rate, number of steps, and sleep patterns using, for example, a wearable device. Heart rate is collected using, for example, a heart rate sensor. For example, the data collection unit monitors heart rate in real time using a heart rate sensor. The data collection unit can also collect step counts using a pedometer. For example, the data collection unit records the number of steps taken daily using a pedometer. The data collection unit can also collect sleep patterns using a sleep tracker. For example, the data collection unit records sleep quality and sleep duration using a sleep tracker. This enables detailed data collection using the wearable device. Some or all of the above-described processing in the data collection unit may be performed using, for example, AI, or may be performed without AI. For example, the data collection unit can analyze the collected data using AI and generate information for providing customized guidance tailored to individual goals and needs.

[0032] The analysis unit can analyze an individual's exercise habits and eating patterns and propose appropriate exercise plans and meal plans. The analysis unit, for example, analyzes an individual's exercise habits and eating patterns. Exercise habits include, for example, exercise frequency and type of exercise. For example, the analysis unit can analyze exercise frequency and propose an optimal exercise plan. The analysis unit can also analyze the type of exercise and propose an exercise plan tailored to individual needs. Meal patterns include, for example, meal frequency and meal content. For example, the analysis unit can analyze meal frequency and propose an optimal meal plan. The analysis unit can also analyze meal content and propose a nutritionally balanced meal plan. This makes it possible to propose an optimal plan based on an individual's exercise habits and eating patterns. 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 analyze collected data using AI and propose an optimal exercise plan and meal plan.

[0033] The advice providing unit can provide exercise and diet advice tailored to individual goals. The advice providing unit provides, for example, exercise and diet advice tailored to individual goals. Individual goals include, for example, weight loss goals and health maintenance goals. For example, the advice providing unit provides an exercise plan tailored to a weight loss goal. The advice providing unit can also provide a diet plan tailored to a health maintenance goal. The exercise advice includes, for example, the type of exercise and the frequency of exercise. For example, the advice providing unit can suggest a type of exercise tailored to individual needs. The advice providing unit can also suggest the frequency of exercise. The diet advice includes, for example, the content of meals and the timing of meals. For example, the advice providing unit can suggest the content of nutritionally balanced meals. The advice providing unit can also suggest the timing of meals. This allows for the provision of advice tailored to individual goals, thereby promoting personal growth. Some or all of the above-described processing by the advice providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the advice providing unit can provide optimal advice and guidance based on information generated using AI.

[0034] The resource management unit can monitor heart rate and sleep patterns in real time using a wearable device to grasp health conditions. The resource management unit monitors heart rate and sleep patterns in real time using, for example, a wearable device. Heart rate is monitored in real time using, for example, a heart rate sensor. For example, the resource management unit monitors heart rate in real time using a heart rate sensor. The resource management unit can also monitor sleep patterns in real time using a sleep tracker. For example, the resource management unit monitors sleep quality and sleep duration in real time using a sleep tracker. This allows for understanding health conditions in real time and providing appropriate advice. Some or all of the above-described processing in the resource management unit may be performed using, for example, AI, or may be performed without using AI. For example, the resource management unit can select an optimal management method based on data collected using AI.

[0035] The data collection unit can analyze the user's past behavioral patterns and select the optimal data collection method. The data collection unit, for example, analyzes the user's past behavioral patterns and selects the optimal data collection method. Past behavioral patterns include, for example, past movement patterns, exercise habits, eating habits, and sleeping habits. For example, the data collection unit analyzes the user's past movement patterns and selects the optimal data collection method. The data collection unit can also analyze the user's past exercise habits and select the optimal data collection method. Furthermore, the data collection unit can analyze the user's past eating habits and select the optimal data collection method. This allows the optimal data collection method to be selected based on the user's past behavioral patterns. Some or all of the above-described processing in the data collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the data collection unit can analyze the user's past behavioral patterns using AI and select the optimal data collection method.

[0036] The data collection unit can perform filtering based on the user's current living situation and areas of interest when collecting data. For example, the data collection unit performs filtering based on the user's current living situation and areas of interest when collecting data. Living situations include, for example, a home environment, a work environment, etc. For example, the data collection unit filters data based on the home environment. The data collection unit can also filter data based on the work environment. Areas of interest include, for example, hobbies and topics of interest. For example, the data collection unit filters data based on hobbies. The data collection unit can also filter data based on topics of interest. This makes it possible to collect highly relevant data based on the user's living situation and areas of interest. Some or all of the above-described processing in the data collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the data collection unit can analyze the user's living situation and areas of interest using AI and select an optimal data collection method.

[0037] The data collection unit can select the optimal collection means depending on the user's input method when collecting data. The data collection unit, for example, selects the optimal collection means depending on the user's input method when collecting data. Input methods include, for example, voice input, text input, and image input. For example, when voice input is used, the data collection unit prioritizes collecting voice data. Furthermore, when text input is used, the data collection unit can also prioritize collecting text data. Furthermore, when image input is used, the data collection unit can also prioritize collecting image data. This makes it possible to select the optimal data collection means depending on the user's input method. Some or all of the above-mentioned processing in the data collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the data collection unit can analyze the user's input method using AI and select the optimal data collection means.

[0038] The data collection unit can prioritize collecting highly relevant data by taking into account the user's geographical location information when collecting data. For example, the data collection unit prioritizes collecting highly relevant data by taking into account the user's geographical location information when collecting data. Geographical location information includes, for example, GPS data. For example, the data collection unit identifies the user's current location using the GPS data and prioritizes collecting data related to that area. Furthermore, when the user is moving, the data collection unit can also prioritize collecting data related to the user's destination. Furthermore, the data collection unit can also prioritize collecting highly relevant data based on the user's current location. This allows highly relevant data to be prioritized based on the user's geographical location information. Some or all of the above-described processing in the data collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the data collection unit can analyze the geographical location information using AI and select an optimal data collection method.

[0039] The data collection unit can analyze the user's social media activities and collect related data when collecting data. For example, the data collection unit can analyze the user's social media activities and collect related data when collecting data. Social media activities include, for example, the content of posts and the frequency of activities. For example, the data collection unit can collect related data based on information shared by the user on social media. The data collection unit can also analyze the user's social media activities and collect highly relevant data. Furthermore, the data collection unit can collect related data by referring to the activities of the user's friends on social media. In this way, related data can be collected based on the user's social media activities. Some or all of the above-described processing in the data collection unit can be performed using, for example, AI, or can be performed without using AI. For example, the data collection unit can analyze the social media activities using AI and select the optimal data collection method.

[0040] The data collection unit can customize the collection method by reflecting the user's past feedback when collecting data. For example, the data collection unit customizes the collection method by reflecting the user's past feedback when collecting data. Past feedback includes, for example, the user's evaluation comments and analysis results of past data. For example, the data collection unit adjusts the data collection method based on feedback provided by the user in the past. The data collection unit can also customize the type of data to be collected by reflecting the user's past feedback. Furthermore, the data collection unit can adjust the timing and frequency of data collection based on the user's feedback. This allows the collection method to be customized based on the user's past feedback. Some or all of the above-mentioned processing in the data collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the data collection unit can analyze past feedback using AI and select the optimal data collection method.

[0041] The analysis unit can adjust the level of detail of the analysis based on the importance of the data during analysis. For example, the analysis unit adjusts the level of detail of the analysis based on the importance of the data during analysis. The importance of the data includes, for example, the impact of the data and the reliability of the data. For example, the analysis unit performs a detailed analysis on data with high importance. The analysis unit can also perform a brief analysis on data with low importance. Furthermore, the analysis unit can dynamically adjust the level of detail of the analysis according to the importance of the data. This makes it possible to adjust the level of detail of the 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 evaluate the importance of the data using AI and select the optimal analysis method.

[0042] The analysis unit can apply different analysis algorithms depending on the category of data during analysis. For example, the analysis unit applies different analysis algorithms depending on the category of data during analysis. Data categories include, for example, health data, behavioral data, and lifestyle status data. For example, the analysis unit applies a health-related analysis algorithm to health data. The analysis unit can also apply a behavioral analysis algorithm to behavioral data. Furthermore, the analysis unit can apply an analysis algorithm according to the lifestyle status to lifestyle status data. This allows the application of an optimal analysis algorithm depending on the category 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 classify data categories using AI and select an optimal analysis algorithm.

[0043] The analysis unit can improve the accuracy of the analysis by referring to the user's past analysis results during analysis. For example, the analysis unit can improve the accuracy of the analysis by referring to the user's past analysis results during analysis. Past analysis results include, for example, a method for saving past data and analysis results. For example, the analysis unit can improve the accuracy of the current analysis based on the user's past analysis results. The analysis unit can also adjust the analysis algorithm by referring to the user's past analysis results. Furthermore, the analysis unit can utilize the user's past analysis results to perform a more accurate analysis. In this way, the accuracy of the analysis can be improved by referring to the past analysis results. Some or all of the above-described processing in the analysis unit can be performed using, for example, AI, or can be performed without using AI. For example, the analysis unit can analyze past analysis results using AI and select an optimal analysis method.

[0044] The analysis unit can determine the priority of analysis based on the time of data submission during analysis. The analysis unit, for example, determines the priority of analysis based on the time of data submission during analysis. The time of data submission includes, for example, a submission deadline and a submission frequency. For example, the analysis unit prioritizes analysis of the most recent data. The analysis unit can also postpone analysis of data that was submitted earlier. Furthermore, the analysis unit can dynamically adjust the priority of analysis based on the time of data submission. This makes it possible to determine the priority of analysis based on the time of data submission. 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 evaluate the time of data submission using AI and select the optimal analysis method.

[0045] The analysis unit can adjust the order of analysis based on the relevance of the data during analysis. The analysis unit, for example, adjusts the order of analysis based on the relevance of the data during analysis. Data relevance includes, for example, data correlation and data co-occurrence. For example, the analysis unit prioritizes analysis of highly relevant data. The analysis unit can also postpone analysis of less relevant data. Furthermore, the analysis unit can dynamically adjust the order of analysis based on the relevance of the data. This makes it possible to adjust the order of analysis based on the relevance 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 evaluate the relevance of the data using AI and select the optimal analysis method.

[0046] The analysis unit can adjust the use of technical terms in the analysis according to the user's level of expertise during analysis. For example, the analysis unit can adjust the use of technical terms in the analysis according to the user's level of expertise during analysis. The level of expertise includes, for example, the user's occupation, educational background, and past experience. For example, if the user has technical expertise, the analysis unit can provide analysis results that use a lot of technical terms. Furthermore, if the user does not have technical expertise, the analysis unit can provide concise and easy-to-understand analysis results. Furthermore, the analysis unit can adjust the way in which the analysis results are presented according to the user's level of expertise. This allows the way in which the analysis results are presented to be adjusted according to the user's level of expertise. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can use AI to evaluate the user's level of expertise and select an optimal analysis method.

[0047] The advice providing unit can adjust the level of detail of the advice based on the user's level of goal achievement when providing advice. For example, the advice providing unit adjusts the level of detail of the advice based on the user's level of goal achievement when providing advice. The level of goal achievement includes, for example, the goal achievement rate and progress. For example, the advice providing unit provides detailed advice when the user is approaching the goal. Furthermore, the advice providing unit can also provide concise advice when the user is moving away from the goal. Furthermore, the advice providing unit can dynamically adjust the level of detail of the advice according to the user's level of goal achievement. This makes it possible to adjust the level of detail of the advice according to the user's level of goal achievement. Some or all of the above-described processing in the advice providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the advice providing unit can evaluate the user's level of goal achievement using AI and select the optimal advice method.

[0048] The advice providing unit can provide optimal advice according to the user's lifestyle rhythm when providing advice. The advice providing unit, for example, provides optimal advice according to the user's lifestyle rhythm when providing advice. Lifestyle rhythms include, for example, daily activity patterns and sleep cycles. For example, if the user is a morning person, the advice providing unit can provide optimal advice for the morning. Also, if the user is a night owl, the advice providing unit can provide optimal advice for the evening. Furthermore, the advice providing unit can adjust the timing of providing the advice according to the user's lifestyle rhythm. This makes it possible to provide optimal advice according to the user's lifestyle rhythm. Some or all of the above-described processing in the advice providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the advice providing unit can analyze the user's lifestyle rhythm using AI and select the optimal advice method.

[0049] The advice providing unit can improve the accuracy of advice by referring to the user's past advice results when providing advice. For example, the advice providing unit improves the accuracy of advice by referring to the user's past advice results when providing advice. Past advice results include, for example, past feedback, the effect of the advice, etc. For example, the advice providing unit improves the accuracy of current advice based on the user's past advice results. The advice providing unit can also adjust the content of the advice by referring to the user's past advice results. Furthermore, the advice providing unit can provide more accurate advice by utilizing the user's past advice results. In this way, the accuracy of advice can be improved by referring to the past advice results. Some or all of the above-described processing in the advice providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the advice providing unit can analyze past advice results using AI and select the optimal advice method.

[0050] The advice providing unit can determine the priority of advice based on the user's current living situation when providing advice. The advice providing unit, for example, determines the priority of advice based on the user's current living situation when providing advice. Living situations include, for example, a home environment, a work environment, etc. For example, when the user is busy, the advice providing unit prioritizes providing important advice. The advice providing unit can also provide detailed advice when the user is relaxed. Furthermore, the advice providing unit can dynamically adjust the priority of advice according to the user's living situation. This makes it possible to determine the priority of advice according to the user's living situation. Some or all of the above-described processing in the advice providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the advice providing unit can analyze the user's living situation using AI and select the optimal advice method.

[0051] The advice providing unit can customize the content of the advice based on the user's interests and concerns when providing advice. The advice providing unit, for example, customizes the content of the advice based on the user's interests and concerns when providing advice. Interests and concerns include, for example, hobbies and topics of interest. For example, the advice providing unit provides advice related to areas in which the user is interested. The advice providing unit can also customize the content of the advice based on the user's interests. Furthermore, the advice providing unit can provide optimal advice by referring to the user's past interests and concerns. This makes it possible to customize the content of the advice based on the user's interests and concerns. Some or all of the above-described processing in the advice providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the advice providing unit can analyze the user's interests and concerns using AI and select the optimal advice method.

[0052] The advice providing unit can improve the advice method by reflecting user feedback when providing advice. The advice providing unit, for example, improves the advice method by reflecting user feedback when providing advice. The feedback includes, for example, user evaluation comments and analysis results of past data. For example, the advice providing unit adjusts the advice method based on the feedback provided by the user. The advice providing unit can also customize the content of the advice by reflecting user feedback. Furthermore, the advice providing unit can improve the accuracy of the advice by utilizing user feedback. This makes it possible to improve the advice method based on user feedback. Some or all of the above-described processing in the advice providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the advice providing unit can analyze user feedback using AI and select the optimal advice method.

[0053] The resource management unit can analyze the user's past health data and select the optimal management method during resource management. For example, the resource management unit analyzes the user's past health data and selects the optimal management method during resource management. The past health data includes, for example, past diagnostic results and health monitoring data. For example, the resource management unit selects the optimal resource management method based on the user's past health data. The resource management unit can also analyze the user's past health data and concentrate resource management during specific time periods. Furthermore, the resource management unit can adjust the frequency and method of resource management based on the user's health data. This allows the optimal resource management method to be selected based on the past health data. Some or all of the above-described processing in the resource management unit may be performed using, for example, AI, or may be performed without using AI. For example, the resource management unit can analyze past health data using AI and select the optimal resource management method.

[0054] The resource management unit can customize the management means based on the user's current health condition during resource management. For example, the resource management unit customizes the management means based on the user's current health condition during resource management. The current health condition includes, for example, current diagnosis results and health monitoring data. For example, if the user is tired, the resource management unit reduces the frequency of resource management to reduce the user's burden. Furthermore, if the user is in good health, the resource management unit can perform detailed resource management. Furthermore, the resource management unit can customize the resource management method based on the user's current health condition. This allows the resource management method to be customized based on the current health condition. Some or all of the above-described processing in the resource management unit may be performed using, for example, AI, or may be performed without AI. For example, the resource management unit can analyze the user's current health condition using AI and select the optimal resource management method.

[0055] The resource management unit can improve the resource management method by reflecting user feedback during resource management. For example, the resource management unit improves the resource management method by reflecting user feedback during resource management. Feedback includes, for example, user evaluation comments and analysis results of past data. For example, the resource management unit adjusts the resource management method based on feedback provided by the user. The resource management unit can also customize the types of resources to be managed by reflecting user feedback. Furthermore, the resource management unit can adjust the timing and frequency of resource management based on user feedback. This allows the resource management method to be improved based on user feedback. Some or all of the above-described processing in the resource management unit may be performed using, for example, AI, or may be performed without using AI. For example, the resource management unit can analyze user feedback using AI and select the optimal resource management method.

[0056] The resource management unit can select an optimal management method by taking into account the user's geographical location information when managing resources. For example, the resource management unit selects an optimal management method by taking into account the user's geographical location information when managing resources. Geographical location information includes, for example, GPS data. For example, the resource management unit identifies the user's current location using the GPS data and prioritizes management of resources related to that area. Furthermore, when the user is moving, the resource management unit can also prioritize management of resources related to the user's destination. Furthermore, the resource management unit can select an optimal resource management method based on the user's current location. This allows the optimal resource management method to be selected based on the geographical location information. Some or all of the above-described processing in the resource management unit may be performed using, for example, AI, or may be performed without using AI. For example, the resource management unit can analyze the geographical location information using AI and select an optimal resource management method.

[0057] The resource management unit can analyze a user's social media activity and suggest management measures during resource management. For example, the resource management unit analyzes a user's social media activity and suggests management measures during resource management. Social media activity includes, for example, the content of posts and the frequency of activity. For example, the resource management unit suggests relevant resource management measures based on information shared by the user on social media. The resource management unit can also analyze the user's social media activity and suggest highly relevant resource management measures. Furthermore, the resource management unit can suggest relevant resource management measures based on the activity of the user's friends on social media. In this way, relevant resource management measures can be suggested based on social media activity. Some or all of the above-described processing in the resource management unit may be performed using, for example, AI, or may be performed without using AI. For example, the resource management unit can analyze social media activity using AI and select an optimal resource management method.

[0058] The resource management unit can customize the management method by reflecting the user's past feedback during resource management. For example, the resource management unit customizes the management method by reflecting the user's past feedback during resource management. Past feedback includes, for example, the user's evaluation comments and analysis results of past data. For example, the resource management unit adjusts the resource management method based on feedback provided by the user in the past. The resource management unit can also customize the types of resources to be managed by reflecting the user's past feedback. Furthermore, the resource management unit can adjust the timing and frequency of resource management based on the user's feedback. This allows the resource management method to be customized based on the past feedback. Some or all of the above-described processing in the resource management unit may be performed using, for example, AI, or may be performed without using AI. For example, the resource management unit can analyze the user's feedback using AI and select the optimal resource management method.

[0059] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0060] The customized coaching system can predict future behavior based on a user's past behavioral data and provide advice based on the predictions. For example, it can analyze past exercise data to predict periods when the user is likely to neglect exercise and provide advice to encourage exercise at those times. It can also predict periods when nutritional imbalance is likely to occur based on past dietary data and suggest balanced meals for those times. It can also analyze past sleep data to predict periods when sleep deprivation is likely to occur and provide advice to encourage appropriate sleep. This makes it possible to provide preventative advice based on the user's behavioral patterns.

[0061] The custom guidance system can provide region-specific health information and advice based on the user's geographic location information. For example, if the user lives at high altitude, the system can provide high altitude-specific exercise advice. If the user lives by the sea, the system can provide advice on exercise and diet for the sea. Furthermore, if the user lives in an urban area, the system can provide urban-specific stress management advice. This allows the system to provide appropriate advice according to the user's geographic location.

[0062] The customized guidance system can adjust the timing of providing advice based on the user's lifestyle. For example, if the user is a nocturnal person, advice can be provided at night. Alternatively, if the user is a morning person, advice can be provided in the morning. Furthermore, if the user's lifestyle is irregular, the timing of providing advice can be adjusted to match that rhythm. This allows advice to be provided at an appropriate time according to the user's lifestyle.

[0063] The custom teaching system can improve the content of advice based on the user's past feedback. For example, it can analyze feedback provided by the user in the past and evaluate the effectiveness of the advice. It can also improve advice that the user has previously expressed dissatisfaction with and provide more effective advice. It can also provide similar advice based on advice that the user has previously rated highly. This allows the content of advice to be continuously improved based on the user's feedback.

[0064] The custom guidance system can analyze a user's social media activity and provide relevant advice. For example, health advice can be provided based on information the user has shared on social media. It can also provide relevant advice based on topics the user has shown interest in on social media. It can also provide similar advice based on the activities of the user's friends on social media. This allows the system to provide highly relevant advice based on the user's social media activity.

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

[0066] Step 1: The data collection unit collects an individual's behavioral patterns, habits, and lifestyle. Specifically, this includes daily movement patterns, exercise habits, eating habits, sleep habits, home environment, and work environment. The data collection unit uses a wearable device to collect data such as heart rate, step count, and sleep patterns. It can also collect an individual's behavioral patterns, habits, and lifestyle using a smartphone or IoT device. For example, daily movement patterns can be collected using the GPS function of a smartphone, and data on the home and work environment can be collected using an IoT device. Step 2: The analysis unit analyzes the collected data and generates information to provide customized guidance tailored to individual goals and needs. The analysis is performed, for example, using AI. Specifically, it analyzes an individual's exercise habits and eating patterns and suggests optimal exercise and eating plans. It can also analyze an individual's sleep patterns and suggest optimal sleep plans. It can also analyze an individual's stress level and provide advice on stress reduction. Step 3: The advice provider provides optimal advice and guidance based on the generated information. Specifically, this includes exercise and dietary advice tailored to the individual's goals. For example, it can provide exercise plans, diet plans, and sleep plans tailored to the individual's goals. The processing in the advice provider may be performed using AI or without AI.

[0067] (Example 2) A customized instruction system according to an embodiment of the present invention collects and analyzes an individual's behavioral patterns, habits, and lifestyle circumstances, and provides customized instruction tailored to the individual's goals and needs. The customized instruction system collects and analyzes an individual's behavioral patterns, habits, and lifestyle circumstances, generates information for providing customized instruction tailored to the individual's goals and needs, and provides optimal advice and guidance. The customized instruction system also works with IoT devices and wearable devices to automatically collect logs, manage health status, perform various analyses, and provide improvement advice. For example, the customized instruction system collects an individual's behavioral patterns, habits, and lifestyle circumstances. For example, the customized instruction system uses a wearable device to collect data such as heart rate, step count, and sleep patterns. The customized instruction system then analyzes the collected data. AI analyzes the collected data and generates information for providing customized instruction tailored to the individual's goals and needs. For example, the AI ​​analyzes an individual's exercise habits and eating patterns and proposes optimal exercise and meal plans. The customized instruction system then provides optimal advice and guidance based on the generated information. For example, the customized instruction system provides exercise and dietary advice tailored to the individual's goals. The customized guidance system also works with IoT and wearable devices to automatically collect logs, manage health status, perform various analyses, and provide advice on improvement. For example, the customized guidance system uses a wearable device to monitor heart rate and sleep patterns in real time to understand health status. This allows the customized guidance system to understand an individual's health status in real time and provide appropriate advice. This allows the customized guidance system to provide customized guidance based on an individual's behavioral patterns, habits, and lifestyle. For example, it can understand an individual's health status in real time and provide appropriate advice. It can also promote personal growth by providing exercise and dietary advice tailored to an individual's goals.

[0068] A customized instruction system according to an embodiment includes a data collection unit, an analysis unit, and an advice provision unit. The data collection unit collects an individual's behavioral patterns, habits, and lifestyle. Examples of the individual's behavioral patterns, habits, and lifestyle include, but are not limited to, daily movement patterns, exercise habits, eating habits, sleep habits, home environment, and work environment. The data collection unit collects data such as heart rate, step count, and sleep patterns using a wearable device. The data collection unit can also collect the individual's behavioral patterns, habits, and lifestyle using a smartphone or IoT device. For example, the data collection unit collects daily movement patterns using the GPS function of a smartphone. The data collection unit can also collect data on home and work environments using an IoT device. The analysis unit analyzes the collected data and generates information for providing customized instruction tailored to individual goals and needs. The analysis can be performed using, for example, AI, but is not limited to, an example. For example, the analysis unit can analyze an individual's exercise habits and eating patterns using AI to propose optimal exercise and meal plans. The analysis unit can also use AI to analyze an individual's sleep patterns and propose an optimal sleep plan. The analysis unit can also use AI to analyze an individual's stress level and provide advice for stress reduction. For example, the analysis unit can use AI to analyze data such as heart rate, step count, and sleep patterns, and generate information for providing customized guidance tailored to individual goals and needs. The advice providing unit provides optimal advice and guidance based on the generated information. The advice includes, but is not limited to, exercise and diet advice tailored to the individual's goals. For example, the advice providing unit can provide an exercise plan tailored to the individual's goals. The advice providing unit can also provide a meal plan tailored to the individual's goals. The advice providing unit can also provide a sleep plan tailored to the individual's goals. For example, the advice providing unit can provide exercise and diet advice tailored to the individual's goals. As a result, the customized guidance system according to the embodiment can provide customized guidance based on an individual's behavioral patterns, habits, and lifestyle.Some or all of the above-described processing in the advice providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the advice providing unit may provide optimal advice or guidance based on information generated using AI.

[0069] The customized guidance system includes a resource management unit that works in conjunction with IoT devices and wearable devices to automatically collect logs, manage health status, perform multiple analyses, and provide improvement advice. The resource management unit works in conjunction with IoT devices and wearable devices to automatically collect logs. The logs include, but are not limited to, heart rate, step count, and sleep data. The resource management unit collects heart rate, step count, and sleep data using, for example, a wearable device. The resource management unit can also collect data on home and work environments using IoT devices. For example, the resource management unit collects home environment data using smart home devices. The resource management unit can also collect work environment data using smart office devices. The resource management unit manages health status and performs multiple analyses based on the collected logs. The analysis is performed using, for example, AI, but is not limited to, an example. For example, the resource management unit uses AI to analyze heart rate, step count, and sleep data to manage health status. The resource management unit can also use AI to analyze home and work environment data to manage health status. The resource management unit provides improvement advice based on the collected logs. The advice includes, but is not limited to, advice on improving exercise and diet. For example, the resource management unit may analyze the collected data using AI and propose optimal exercise and meal plans. The resource management unit may also analyze the collected data using AI and provide advice on stress reduction. This enables health status management and improvement advice by linking with IoT and wearable devices. Some or all of the above-described processing in the resource management unit may be performed using AI, for example, or may be performed without using AI. For example, the resource management unit may select an optimal management method based on data collected using AI.

[0070] The data collection unit can collect data such as heart rate, number of steps, and sleep patterns using a wearable device. The data collection unit collects data such as heart rate, number of steps, and sleep patterns using, for example, a wearable device. Heart rate is collected using, for example, a heart rate sensor. For example, the data collection unit monitors heart rate in real time using a heart rate sensor. The data collection unit can also collect step counts using a pedometer. For example, the data collection unit records the number of steps taken daily using a pedometer. The data collection unit can also collect sleep patterns using a sleep tracker. For example, the data collection unit records sleep quality and sleep duration using a sleep tracker. This enables detailed data collection using the wearable device. Some or all of the above-described processing in the data collection unit may be performed using, for example, AI, or may be performed without AI. For example, the data collection unit can analyze the collected data using AI and generate information for providing customized guidance tailored to individual goals and needs.

[0071] The analysis unit can analyze an individual's exercise habits and eating patterns and propose appropriate exercise plans and meal plans. The analysis unit, for example, analyzes an individual's exercise habits and eating patterns. Exercise habits include, for example, exercise frequency and type of exercise. For example, the analysis unit can analyze exercise frequency and propose an optimal exercise plan. The analysis unit can also analyze the type of exercise and propose an exercise plan tailored to individual needs. Meal patterns include, for example, meal frequency and meal content. For example, the analysis unit can analyze meal frequency and propose an optimal meal plan. The analysis unit can also analyze meal content and propose a nutritionally balanced meal plan. This makes it possible to propose an optimal plan based on an individual's exercise habits and eating patterns. 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 analyze collected data using AI and propose an optimal exercise plan and meal plan.

[0072] The advice providing unit can provide exercise and diet advice tailored to individual goals. The advice providing unit provides, for example, exercise and diet advice tailored to individual goals. Individual goals include, for example, weight loss goals and health maintenance goals. For example, the advice providing unit provides an exercise plan tailored to a weight loss goal. The advice providing unit can also provide a diet plan tailored to a health maintenance goal. The exercise advice includes, for example, the type of exercise and the frequency of exercise. For example, the advice providing unit can suggest a type of exercise tailored to individual needs. The advice providing unit can also suggest the frequency of exercise. The diet advice includes, for example, the content of meals and the timing of meals. For example, the advice providing unit can suggest the content of nutritionally balanced meals. The advice providing unit can also suggest the timing of meals. This allows for the provision of advice tailored to individual goals, thereby promoting personal growth. Some or all of the above-described processing by the advice providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the advice providing unit can provide optimal advice and guidance based on information generated using AI.

[0073] The resource management unit can monitor heart rate and sleep patterns in real time using a wearable device to grasp health conditions. The resource management unit monitors heart rate and sleep patterns in real time using, for example, a wearable device. Heart rate is monitored in real time using, for example, a heart rate sensor. For example, the resource management unit monitors heart rate in real time using a heart rate sensor. The resource management unit can also monitor sleep patterns in real time using a sleep tracker. For example, the resource management unit monitors sleep quality and sleep duration in real time using a sleep tracker. This allows for understanding health conditions in real time and providing appropriate advice. Some or all of the above-described processing in the resource management unit may be performed using, for example, AI, or may be performed without using AI. For example, the resource management unit can select an optimal management method based on data collected using AI.

[0074] The data collection unit can estimate the user's emotions and adjust the timing of data collection based on the estimated user emotions. The data collection unit, for example, estimates the user's emotions and adjusts the timing of data collection based on the estimated user emotions. Emotion estimation is performed, for example, using an emotion recognition algorithm. For example, the data collection unit estimates the user's emotions using an emotion recognition algorithm. The data collection unit can also adjust the timing of data collection based on the user's emotions. For example, if the user is stressed, the frequency of data collection can be reduced to reduce the user's burden. Also, if the user is relaxed, the collection frequency can be increased to collect detailed data. Furthermore, if the user is in a hurry, only important data can be collected preferentially. This reduces the user's burden by adjusting the timing of data collection according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the data collection unit can be performed, for example, using AI or without AI. For example, the data collection unit can select the optimal data collection method based on data collected using AI.

[0075] The data collection unit can analyze the user's past behavioral patterns and select the optimal data collection method. The data collection unit, for example, analyzes the user's past behavioral patterns and selects the optimal data collection method. Past behavioral patterns include, for example, past movement patterns, exercise habits, eating habits, and sleeping habits. For example, the data collection unit analyzes the user's past movement patterns and selects the optimal data collection method. The data collection unit can also analyze the user's past exercise habits and select the optimal data collection method. Furthermore, the data collection unit can analyze the user's past eating habits and select the optimal data collection method. This allows the optimal data collection method to be selected based on the user's past behavioral patterns. Some or all of the above-described processing in the data collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the data collection unit can analyze the user's past behavioral patterns using AI and select the optimal data collection method.

[0076] The data collection unit can perform filtering based on the user's current living situation and areas of interest when collecting data. For example, the data collection unit performs filtering based on the user's current living situation and areas of interest when collecting data. Living situations include, for example, a home environment, a work environment, etc. For example, the data collection unit filters data based on the home environment. The data collection unit can also filter data based on the work environment. Areas of interest include, for example, hobbies and topics of interest. For example, the data collection unit filters data based on hobbies. The data collection unit can also filter data based on topics of interest. This makes it possible to collect highly relevant data based on the user's living situation and areas of interest. Some or all of the above-described processing in the data collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the data collection unit can analyze the user's living situation and areas of interest using AI and select an optimal data collection method.

[0077] The data collection unit can select the optimal collection means depending on the user's input method when collecting data. The data collection unit, for example, selects the optimal collection means depending on the user's input method when collecting data. Input methods include, for example, voice input, text input, and image input. For example, when voice input is used, the data collection unit prioritizes collecting voice data. Furthermore, when text input is used, the data collection unit can also prioritize collecting text data. Furthermore, when image input is used, the data collection unit can also prioritize collecting image data. This makes it possible to select the optimal data collection means depending on the user's input method. Some or all of the above-mentioned processing in the data collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the data collection unit can analyze the user's input method using AI and select the optimal data collection means.

[0078] The data collection unit can estimate the user's emotions and prioritize the data to be collected based on the estimated user emotions. The data collection unit, for example, estimates the user's emotions and prioritizes the data to be collected based on the estimated user emotions. Emotion estimation is performed, for example, using an emotion recognition algorithm. For example, the data collection unit estimates the user's emotions using an emotion recognition algorithm. The data collection unit can also prioritize the data to be collected based on the user's emotions. For example, if the user is feeling stressed, data related to stress reduction can be collected preferentially. Furthermore, if the user is relaxed, the priority can be adjusted to collect detailed data. Furthermore, if the user is in a hurry, only important data can be collected preferentially. In this way, the priority of the data to be collected can be determined according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-mentioned processing in the data collection unit can be performed, for example, using AI or without AI. For example, the data collection unit can select the optimal data collection method based on data collected using AI.

[0079] The data collection unit can prioritize collecting highly relevant data by taking into account the user's geographical location information when collecting data. For example, the data collection unit prioritizes collecting highly relevant data by taking into account the user's geographical location information when collecting data. Geographical location information includes, for example, GPS data. For example, the data collection unit identifies the user's current location using the GPS data and prioritizes collecting data related to that area. Furthermore, when the user is moving, the data collection unit can also prioritize collecting data related to the user's destination. Furthermore, the data collection unit can also prioritize collecting highly relevant data based on the user's current location. This allows highly relevant data to be prioritized based on the user's geographical location information. Some or all of the above-described processing in the data collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the data collection unit can analyze the geographical location information using AI and select an optimal data collection method.

[0080] The data collection unit can analyze the user's social media activities and collect related data when collecting data. For example, the data collection unit can analyze the user's social media activities and collect related data when collecting data. Social media activities include, for example, the content of posts and the frequency of activities. For example, the data collection unit can collect related data based on information shared by the user on social media. The data collection unit can also analyze the user's social media activities and collect highly relevant data. Furthermore, the data collection unit can collect related data by referring to the activities of the user's friends on social media. In this way, related data can be collected based on the user's social media activities. Some or all of the above-described processing in the data collection unit can be performed using, for example, AI, or can be performed without using AI. For example, the data collection unit can analyze the social media activities using AI and select the optimal data collection method.

[0081] The data collection unit can customize the collection method by reflecting the user's past feedback when collecting data. For example, the data collection unit customizes the collection method by reflecting the user's past feedback when collecting data. Past feedback includes, for example, the user's evaluation comments and analysis results of past data. For example, the data collection unit adjusts the data collection method based on feedback provided by the user in the past. The data collection unit can also customize the type of data to be collected by reflecting the user's past feedback. Furthermore, the data collection unit can adjust the timing and frequency of data collection based on the user's feedback. This allows the collection method to be customized based on the user's past feedback. Some or all of the above-mentioned processing in the data collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the data collection unit can analyze past feedback using AI and select the optimal data collection method.

[0082] The analysis unit can estimate the user's emotion and adjust the presentation method of the analysis based on the estimated user's emotion. The analysis unit, for example, estimates the user's emotion and adjusts the presentation method of the analysis based on the estimated user's emotion. Emotion estimation is performed, for example, using an emotion recognition algorithm. For example, the analysis unit estimates the user's emotion using an emotion recognition algorithm. The analysis unit can also adjust the presentation method of the analysis based on the user's emotion. For example, if the user is nervous, a simple and highly visible analysis result can be provided. On the other hand, if the user is relaxed, a detailed analysis result can be provided. Furthermore, if the user is in a hurry, a concise analysis result that focuses on the main points can be provided. This allows the presentation method of the analysis to be adjusted according to the user's emotion. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the analysis unit can be performed, for example, using AI or without AI. For example, the analysis department can select the most appropriate analysis method based on data collected using AI.

[0083] The analysis unit can adjust the level of detail of the analysis based on the importance of the data during analysis. For example, the analysis unit adjusts the level of detail of the analysis based on the importance of the data during analysis. The importance of the data includes, for example, the impact of the data and the reliability of the data. For example, the analysis unit performs a detailed analysis on data with high importance. The analysis unit can also perform a brief analysis on data with low importance. Furthermore, the analysis unit can dynamically adjust the level of detail of the analysis according to the importance of the data. This makes it possible to adjust the level of detail of the 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 evaluate the importance of the data using AI and select the optimal analysis method.

[0084] The analysis unit can apply different analysis algorithms depending on the category of data during analysis. For example, the analysis unit applies different analysis algorithms depending on the category of data during analysis. Data categories include, for example, health data, behavioral data, and lifestyle status data. For example, the analysis unit applies a health-related analysis algorithm to health data. The analysis unit can also apply a behavioral analysis algorithm to behavioral data. Furthermore, the analysis unit can apply an analysis algorithm according to the lifestyle status to lifestyle status data. This allows the application of an optimal analysis algorithm depending on the category 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 classify data categories using AI and select an optimal analysis algorithm.

[0085] The analysis unit can improve the accuracy of the analysis by referring to the user's past analysis results during analysis. For example, the analysis unit can improve the accuracy of the analysis by referring to the user's past analysis results during analysis. Past analysis results include, for example, a method for saving past data and analysis results. For example, the analysis unit can improve the accuracy of the current analysis based on the user's past analysis results. The analysis unit can also adjust the analysis algorithm by referring to the user's past analysis results. Furthermore, the analysis unit can utilize the user's past analysis results to perform a more accurate analysis. In this way, the accuracy of the analysis can be improved by referring to the past analysis results. Some or all of the above-described processing in the analysis unit can be performed using, for example, AI, or can be performed without using AI. For example, the analysis unit can analyze past analysis results using AI and select an optimal analysis method.

[0086] The analysis unit can estimate the user's emotion and adjust the length of the analysis based on the estimated user's emotion. The analysis unit, for example, estimates the user's emotion and adjusts the length of the analysis based on the estimated user's emotion. Emotion estimation is performed, for example, using an emotion recognition algorithm. For example, the analysis unit estimates the user's emotion using an emotion recognition algorithm. The analysis unit can also adjust the length of the analysis based on the user's emotion. For example, if the user is nervous, a short and concise analysis result can be provided. On the other hand, if the user is relaxed, a detailed analysis result can be provided. Furthermore, if the user is in a hurry, a concise analysis result can be provided. This allows the length of the analysis to be adjusted according to the user's emotion. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can select an optimal analysis method based on data collected using AI.

[0087] The analysis unit can determine the priority of analysis based on the time of data submission during analysis. The analysis unit, for example, determines the priority of analysis based on the time of data submission during analysis. The time of data submission includes, for example, a submission deadline and a submission frequency. For example, the analysis unit prioritizes analysis of the most recent data. The analysis unit can also postpone analysis of data that was submitted earlier. Furthermore, the analysis unit can dynamically adjust the priority of analysis based on the time of data submission. This makes it possible to determine the priority of analysis based on the time of data submission. 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 evaluate the time of data submission using AI and select the optimal analysis method.

[0088] The analysis unit can adjust the order of analysis based on the relevance of the data during analysis. The analysis unit, for example, adjusts the order of analysis based on the relevance of the data during analysis. Data relevance includes, for example, data correlation and data co-occurrence. For example, the analysis unit prioritizes analysis of highly relevant data. The analysis unit can also postpone analysis of less relevant data. Furthermore, the analysis unit can dynamically adjust the order of analysis based on the relevance of the data. This makes it possible to adjust the order of analysis based on the relevance 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 evaluate the relevance of the data using AI and select the optimal analysis method.

[0089] The analysis unit can adjust the use of technical terms in the analysis according to the user's level of expertise during analysis. For example, the analysis unit can adjust the use of technical terms in the analysis according to the user's level of expertise during analysis. The level of expertise includes, for example, the user's occupation, educational background, and past experience. For example, if the user has technical expertise, the analysis unit can provide analysis results that use a lot of technical terms. Furthermore, if the user does not have technical expertise, the analysis unit can provide concise and easy-to-understand analysis results. Furthermore, the analysis unit can adjust the way in which the analysis results are presented according to the user's level of expertise. This allows the way in which the analysis results are presented to be adjusted according to the user's level of expertise. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can use AI to evaluate the user's level of expertise and select an optimal analysis method.

[0090] The advice providing unit can estimate the user's emotion and adjust the way the advice is expressed based on the estimated user's emotion. The advice providing unit, for example, estimates the user's emotion and adjusts the way the advice is expressed based on the estimated user's emotion. Emotion estimation is performed, for example, using an emotion recognition algorithm. For example, the advice providing unit estimates the user's emotion using an emotion recognition algorithm. The advice providing unit can also adjust the way the advice is expressed based on the user's emotion. For example, if the user is nervous, simple, highly visible advice can be provided. If the user is relaxed, detailed advice can be provided. Furthermore, if the user is in a hurry, concise advice that focuses on the main points can be provided. This makes it possible to adjust the way the advice is expressed depending on the user's emotion. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-mentioned processing in the advice providing unit can be performed, for example, using AI or without AI. For example, the advice providing unit can select the most appropriate advice method based on data collected using AI.

[0091] The advice providing unit can adjust the level of detail of the advice based on the user's level of goal achievement when providing advice. For example, the advice providing unit adjusts the level of detail of the advice based on the user's level of goal achievement when providing advice. The level of goal achievement includes, for example, the goal achievement rate and progress. For example, the advice providing unit provides detailed advice when the user is approaching the goal. Furthermore, the advice providing unit can also provide concise advice when the user is moving away from the goal. Furthermore, the advice providing unit can dynamically adjust the level of detail of the advice according to the user's level of goal achievement. This makes it possible to adjust the level of detail of the advice according to the user's level of goal achievement. Some or all of the above-described processing in the advice providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the advice providing unit can evaluate the user's level of goal achievement using AI and select the optimal advice method.

[0092] The advice providing unit can provide optimal advice according to the user's lifestyle rhythm when providing advice. The advice providing unit, for example, provides optimal advice according to the user's lifestyle rhythm when providing advice. Lifestyle rhythms include, for example, daily activity patterns and sleep cycles. For example, if the user is a morning person, the advice providing unit can provide optimal advice for the morning. Also, if the user is a night owl, the advice providing unit can provide optimal advice for the evening. Furthermore, the advice providing unit can adjust the timing of providing the advice according to the user's lifestyle rhythm. This makes it possible to provide optimal advice according to the user's lifestyle rhythm. Some or all of the above-described processing in the advice providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the advice providing unit can analyze the user's lifestyle rhythm using AI and select the optimal advice method.

[0093] The advice providing unit can improve the accuracy of advice by referring to the user's past advice results when providing advice. For example, the advice providing unit improves the accuracy of advice by referring to the user's past advice results when providing advice. Past advice results include, for example, past feedback, the effect of the advice, etc. For example, the advice providing unit improves the accuracy of current advice based on the user's past advice results. The advice providing unit can also adjust the content of the advice by referring to the user's past advice results. Furthermore, the advice providing unit can provide more accurate advice by utilizing the user's past advice results. In this way, the accuracy of advice can be improved by referring to the past advice results. Some or all of the above-described processing in the advice providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the advice providing unit can analyze past advice results using AI and select the optimal advice method.

[0094] The advice providing unit can estimate the user's emotion and adjust the length of the advice based on the estimated user's emotion. The advice providing unit, for example, estimates the user's emotion and adjusts the length of the advice based on the estimated user's emotion. The emotion estimation is performed, for example, using an emotion recognition algorithm. For example, the advice providing unit estimates the user's emotion using an emotion recognition algorithm. The advice providing unit can also adjust the length of the advice based on the user's emotion. For example, if the user is nervous, short and to the point advice can be provided. If the user is relaxed, detailed advice can be provided. Furthermore, if the user is in a hurry, concise advice can be provided. This allows the length of the advice to be adjusted according to the user's emotion. The emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-mentioned processing in the advice providing unit may be performed using AI, for example, or without AI. For example, the advice providing unit can select an optimal advice method based on data collected using AI.

[0095] The advice providing unit can determine the priority of advice based on the user's current living situation when providing advice. The advice providing unit, for example, determines the priority of advice based on the user's current living situation when providing advice. Living situations include, for example, a home environment, a work environment, etc. For example, when the user is busy, the advice providing unit prioritizes providing important advice. The advice providing unit can also provide detailed advice when the user is relaxed. Furthermore, the advice providing unit can dynamically adjust the priority of advice according to the user's living situation. This makes it possible to determine the priority of advice according to the user's living situation. Some or all of the above-described processing in the advice providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the advice providing unit can analyze the user's living situation using AI and select the optimal advice method.

[0096] The advice providing unit can customize the content of the advice based on the user's interests and concerns when providing advice. The advice providing unit, for example, customizes the content of the advice based on the user's interests and concerns when providing advice. Interests and concerns include, for example, hobbies and topics of interest. For example, the advice providing unit provides advice related to areas in which the user is interested. The advice providing unit can also customize the content of the advice based on the user's interests. Furthermore, the advice providing unit can provide optimal advice by referring to the user's past interests and concerns. This makes it possible to customize the content of the advice based on the user's interests and concerns. Some or all of the above-described processing in the advice providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the advice providing unit can analyze the user's interests and concerns using AI and select the optimal advice method.

[0097] The advice providing unit can improve the advice method by reflecting user feedback when providing advice. The advice providing unit, for example, improves the advice method by reflecting user feedback when providing advice. The feedback includes, for example, user evaluation comments and analysis results of past data. For example, the advice providing unit adjusts the advice method based on the feedback provided by the user. The advice providing unit can also customize the content of the advice by reflecting user feedback. Furthermore, the advice providing unit can improve the accuracy of the advice by utilizing user feedback. This makes it possible to improve the advice method based on user feedback. Some or all of the above-described processing in the advice providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the advice providing unit can analyze user feedback using AI and select the optimal advice method.

[0098] The resource management unit can estimate a user's emotion and adjust the resource management method based on the estimated user's emotion. The resource management unit, for example, estimates a user's emotion and adjusts the resource management method based on the estimated user's emotion. Emotion estimation is performed, for example, using an emotion recognition algorithm. For example, the resource management unit estimates a user's emotion using an emotion recognition algorithm. The resource management unit can also adjust the resource management method based on the user's emotion. For example, if the user is stressed, the resource management unit can reduce the frequency of resource management to reduce the user's burden. If the user is relaxed, the resource management unit can increase the frequency to perform detailed resource management. Furthermore, if the user is in a hurry, the resource management unit can prioritize management of only important resources. This allows the resource management method to be adjusted according to the user's emotion. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the resource management unit can be performed using, for example, an AI, or without an AI. For example, the resource management unit can select the optimal resource management method based on data collected using AI.

[0099] The resource management unit can analyze the user's past health data and select the optimal management method during resource management. For example, the resource management unit analyzes the user's past health data and selects the optimal management method during resource management. The past health data includes, for example, past diagnostic results and health monitoring data. For example, the resource management unit selects the optimal resource management method based on the user's past health data. The resource management unit can also analyze the user's past health data and concentrate resource management during specific time periods. Furthermore, the resource management unit can adjust the frequency and method of resource management based on the user's health data. This allows the optimal resource management method to be selected based on the past health data. Some or all of the above-described processing in the resource management unit may be performed using, for example, AI, or may be performed without using AI. For example, the resource management unit can analyze past health data using AI and select the optimal resource management method.

[0100] The resource management unit can customize the management means based on the user's current health condition during resource management. For example, the resource management unit customizes the management means based on the user's current health condition during resource management. The current health condition includes, for example, current diagnosis results and health monitoring data. For example, if the user is tired, the resource management unit reduces the frequency of resource management to reduce the user's burden. Furthermore, if the user is in good health, the resource management unit can perform detailed resource management. Furthermore, the resource management unit can customize the resource management method based on the user's current health condition. This allows the resource management method to be customized based on the current health condition. Some or all of the above-described processing in the resource management unit may be performed using, for example, AI, or may be performed without AI. For example, the resource management unit can analyze the user's current health condition using AI and select the optimal resource management method.

[0101] The resource management unit can improve the resource management method by reflecting user feedback during resource management. For example, the resource management unit improves the resource management method by reflecting user feedback during resource management. Feedback includes, for example, user evaluation comments and analysis results of past data. For example, the resource management unit adjusts the resource management method based on feedback provided by the user. The resource management unit can also customize the types of resources to be managed by reflecting user feedback. Furthermore, the resource management unit can adjust the timing and frequency of resource management based on user feedback. This allows the resource management method to be improved based on user feedback. Some or all of the above-described processing in the resource management unit may be performed using, for example, AI, or may be performed without using AI. For example, the resource management unit can analyze user feedback using AI and select the optimal resource management method.

[0102] The resource management unit can estimate a user's emotion and determine resource management priorities based on the estimated user's emotion. The resource management unit, for example, estimates a user's emotion and determines resource management priorities based on the estimated user's emotion. Emotion estimation is performed, for example, using an emotion recognition algorithm. For example, the resource management unit estimates a user's emotion using an emotion recognition algorithm. The resource management unit can also determine resource management priorities based on the user's emotion. For example, if the user is stressed, resources related to stress reduction can be prioritized for management. Furthermore, if the user is relaxed, the priority can be adjusted to perform detailed resource management. Furthermore, if the user is in a hurry, only important resources can be prioritized for management. This allows resource management priorities to be determined according to the user's emotion. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the resource management unit can be performed using, for example, an AI, or without an AI. For example, the resource management unit can select the optimal resource management method based on data collected using AI.

[0103] The resource management unit can select an optimal management method by taking into account the user's geographical location information when managing resources. For example, the resource management unit selects an optimal management method by taking into account the user's geographical location information when managing resources. Geographical location information includes, for example, GPS data. For example, the resource management unit identifies the user's current location using the GPS data and prioritizes management of resources related to that area. Furthermore, when the user is moving, the resource management unit can also prioritize management of resources related to the user's destination. Furthermore, the resource management unit can select an optimal resource management method based on the user's current location. This allows the optimal resource management method to be selected based on the geographical location information. Some or all of the above-described processing in the resource management unit may be performed using, for example, AI, or may be performed without using AI. For example, the resource management unit can analyze the geographical location information using AI and select an optimal resource management method.

[0104] The resource management unit can analyze a user's social media activity and suggest management measures during resource management. For example, the resource management unit analyzes a user's social media activity and suggests management measures during resource management. Social media activity includes, for example, the content of posts and the frequency of activity. For example, the resource management unit suggests relevant resource management measures based on information shared by the user on social media. The resource management unit can also analyze the user's social media activity and suggest highly relevant resource management measures. Furthermore, the resource management unit can suggest relevant resource management measures based on the activity of the user's friends on social media. In this way, relevant resource management measures can be suggested based on social media activity. Some or all of the above-described processing in the resource management unit may be performed using, for example, AI, or may be performed without using AI. For example, the resource management unit can analyze social media activity using AI and select an optimal resource management method.

[0105] The resource management unit can customize the management method by reflecting the user's past feedback during resource management. For example, the resource management unit customizes the management method by reflecting the user's past feedback during resource management. Past feedback includes, for example, the user's evaluation comments and analysis results of past data. For example, the resource management unit adjusts the resource management method based on feedback provided by the user in the past. The resource management unit can also customize the types of resources to be managed by reflecting the user's past feedback. Furthermore, the resource management unit can adjust the timing and frequency of resource management based on the user's feedback. This allows the resource management method to be customized based on the past feedback. Some or all of the above-described processing in the resource management unit may be performed using, for example, AI, or may be performed without using AI. For example, the resource management unit can analyze the user's feedback using AI and select the optimal resource management method. === Hard Collateral 1-1 === Each of the multiple elements, including the data collection unit, analysis unit, advice provision unit, and resource management unit, described above, is realized, for example, in at least one of the smart device 14 and the data processing device 12. For example, the data collection unit collects an individual's behavioral patterns, habits, and living conditions using the camera 42 and microphone 38B of the smart device 14. The analysis unit analyzes the data collected by the specific processing unit 290 of the data processing device 12 and generates information for providing customized guidance tailored to individual goals and needs. The advice provision unit provides optimal advice and guidance based on the information generated by the specific processing unit 290 of the data processing device 12. The resource management unit is realized in at least one of the smart device 14 and the data processing device 12, and works in conjunction with IoT and wearable devices to automatically collect logs, manage health conditions, perform multiple analyses, and provide improvement advice. === Hard Collateral 1-2 === Each of the multiple elements, including the data collection unit, analysis unit, advice provision unit, and resource management unit, described above, is realized, for example, in at least one of the smart glasses 214 and the data processing device 12. For example, the data collection unit collects an individual's behavioral patterns, habits, and living conditions using the camera 42 and microphone 238 of the smart glasses 214. The analysis unit analyzes the data collected by the specific processing unit 290 of the data processing device 12 and generates information for providing customized guidance tailored to individual goals and needs. The advice provision unit provides optimal advice and guidance based on the information generated by the specific processing unit 290 of the data processing device 12. The resource management unit is realized in at least one of the smart glasses 214 and the data processing device 12, and works in conjunction with IoT and wearable devices to automatically collect logs, manage health conditions, perform multiple analyses, and provide improvement advice. === Hard Collateral 1-3 === Each of the multiple elements including the data collection unit, analysis unit, advice provision unit, and resource management unit described above is realized, for example, in at least one of the headset terminal 314 and the data processing device 12. For example, the data collection unit collects an individual's behavioral patterns, habits, and living conditions using the camera 42 and microphone 238 of the headset terminal 314. The analysis unit analyzes the data collected by the specific processing unit 290 of the data processing device 12 and generates information for providing customized guidance tailored to individual goals and needs. The advice provision unit provides optimal advice and guidance based on the information generated by the specific processing unit 290 of the data processing device 12. The resource management unit is realized in at least one of the headset terminal 314 and the data processing device 12, and works in conjunction with IoT and wearable devices to automatically collect logs, manage health conditions, perform multiple analyses, and provide improvement advice. === Hard Collateral 1-4 === Each of the multiple elements including the data collection unit, analysis unit, advice provision unit, and resource management unit described above is realized, for example, in at least one of the robot 414 and the data processing device 12. For example, the data collection unit collects an individual's behavioral patterns, habits, and living conditions using the camera 42 and microphone 238 of the robot 414. The analysis unit analyzes the data collected by the specific processing unit 290 of the data processing device 12 and generates information for providing customized guidance tailored to individual goals and needs. The advice provision unit provides optimal advice and guidance based on the information generated by the specific processing unit 290 of the data processing device 12. The resource management unit is realized in at least one of the robot 414 and the data processing device 12, and works in conjunction with IoT and wearable devices to automatically collect logs, manage health conditions, perform multiple analyses, and provide improvement advice.

[0106] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0107] The custom guidance system can estimate the user's emotions and adjust the content of advice based on the estimated emotions. For example, if the user is feeling stressed, advice to relax can be provided. If the user is feeling motivated, advice to set more challenging goals can be provided. Furthermore, if the user is tired, advice to prioritize rest can be provided. In this way, appropriate advice can be provided according to the user's emotions.

[0108] The customized coaching system can predict future behavior based on a user's past behavioral data and provide advice based on the predictions. For example, it can analyze past exercise data to predict periods when the user is likely to neglect exercise and provide advice to encourage exercise at those times. It can also predict periods when nutritional imbalance is likely to occur based on past dietary data and suggest balanced meals for those times. It can also analyze past sleep data to predict periods when sleep deprivation is likely to occur and provide advice to encourage appropriate sleep. This makes it possible to provide preventative advice based on the user's behavioral patterns.

[0109] The custom teaching system can estimate the user's emotions and adjust the timing of advice based on the estimated emotions. For example, if the user is feeling stressed, the system can temporarily refrain from providing advice. Alternatively, if the user is relaxed, the system can increase the frequency of advice provided. Furthermore, if the user is in a hurry, the system can prioritize providing only important advice. This allows advice to be provided at an appropriate time according to the user's emotions.

[0110] The custom guidance system can provide region-specific health information and advice based on the user's geographic location information. For example, if the user lives at high altitude, the system can provide high altitude-specific exercise advice. If the user lives by the sea, the system can provide advice on exercise and diet for the sea. Furthermore, if the user lives in an urban area, the system can provide urban-specific stress management advice. This allows the system to provide appropriate advice according to the user's geographic location.

[0111] The custom teaching system can estimate the user's emotions and personalize the content of advice based on the estimated emotions. For example, if the user is feeling anxious, advice to make the user feel reassured can be provided. If the user is feeling happy, advice to maintain that emotion can be provided. Furthermore, if the user is feeling angry, advice to stay calm can be provided. In this way, personalized advice can be provided according to the user's emotions.

[0112] The customized guidance system can adjust the timing of providing advice based on the user's lifestyle. For example, if the user is a nocturnal person, advice can be provided at night. Alternatively, if the user is a morning person, advice can be provided in the morning. Furthermore, if the user's lifestyle is irregular, the timing of providing advice can be adjusted to match that rhythm. This allows advice to be provided at an appropriate time according to the user's lifestyle.

[0113] The custom guidance system can estimate the user's emotions and adjust the format of advice based on the estimated emotions. For example, if the user is stressed, concise visual advice can be provided. If the user is relaxed, detailed text advice can be provided. If the user is in a hurry, audio advice can be provided. This allows advice to be provided in an appropriate format depending on the user's emotions.

[0114] The custom teaching system can improve the content of advice based on the user's past feedback. For example, it can analyze feedback provided by the user in the past and evaluate the effectiveness of the advice. It can also improve advice that the user has previously expressed dissatisfaction with and provide more effective advice. It can also provide similar advice based on advice that the user has previously rated highly. This allows the content of advice to be continuously improved based on the user's feedback.

[0115] The custom guidance system can analyze a user's social media activity and provide relevant advice. For example, health advice can be provided based on information the user has shared on social media. It can also provide relevant advice based on topics the user has shown interest in on social media. It can also provide similar advice based on the activities of the user's friends on social media. This allows the system to provide highly relevant advice based on the user's social media activity.

[0116] The custom guidance system can estimate the user's emotions and prioritize advice based on the estimated emotions. For example, if the user is feeling stressed, advice related to stress reduction can be provided with priority. If the user is feeling relaxed, advice related to long-term goals can be provided with priority. Furthermore, if the user is in a hurry, only important advice can be provided with priority. In this way, the priority of advice can be determined according to the user's emotions.

[0117] The processing flow of the second embodiment will be briefly explained below.

[0118] Step 1: The data collection unit collects an individual's behavioral patterns, habits, and lifestyle. Specifically, this includes daily movement patterns, exercise habits, eating habits, sleep habits, home environment, and work environment. The data collection unit uses a wearable device to collect data such as heart rate, step count, and sleep patterns. It can also collect an individual's behavioral patterns, habits, and lifestyle using a smartphone or IoT device. For example, daily movement patterns can be collected using the GPS function of a smartphone, and data on the home and work environment can be collected using an IoT device. Step 2: The analysis unit analyzes the collected data and generates information to provide customized guidance tailored to individual goals and needs. The analysis is performed, for example, using AI. Specifically, it analyzes an individual's exercise habits and eating patterns and suggests optimal exercise and eating plans. It can also analyze an individual's sleep patterns and suggest optimal sleep plans. It can also analyze an individual's stress level and provide advice on stress reduction. Step 3: The advice provider provides optimal advice and guidance based on the generated information. Specifically, this includes exercise and dietary advice tailored to the individual's goals. For example, it can provide exercise plans, diet plans, and sleep plans tailored to the individual's goals. The processing in the advice provider may be performed using AI or without AI.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0123] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0124] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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).

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0139] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0140] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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).

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0155] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0156] 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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).

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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).

[0176] 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.

[0177] 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."

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] 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.

[0189] 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.

[0190] [Explanation of symbols]

[0191] 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 system comprising: a data collection unit that collects an individual's behavioral patterns, habits, and living conditions; an analysis unit that analyzes the data collected by the data collection unit and generates information to provide customized guidance tailored to individual goals and needs; and an advice provision unit that provides appropriate advice and guidance based on the information generated by the analysis unit.

2. The system according to claim 1, characterized in that it comprises a resource management unit that works in conjunction with IoT and wearable devices to automatically collect logs, manage health status, perform multiple analyses, and provide advice on improvement.

3. The data collection unit Wearable devices are used to collect data such as heart rate, steps, and sleep patterns.

2. The system of claim 1.

4. The system according to claim 1 , wherein the analysis unit analyzes an individual's exercise habits and eating patterns and proposes appropriate exercise and eating plans.

5. The advice providing unit Providing exercise and nutrition advice tailored to individual goals 2. The system of claim 1.

6. The resource management unit Use wearable devices to monitor your heart rate and sleep patterns in real time to understand your health.

3. The system of claim 2.

7. The data collection unit Inferring user emotions and adjusting the timing of data collection based on the estimated user emotions 2. The system of claim 1.

8. The data collection unit Analyze users' past behavioral patterns and select the most appropriate data collection method 2. The system of claim 1.

9. The data collection unit Filtering data collection based on the user's current life situation and interests 2. The system of claim 1.

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