system

Abstract

An object of the system according to the embodiment is to specifically evaluate how much the daily behavior of the user contributes to the SDGs and provide the evaluation as a score.SOLUTION: A system according to an embodiment includes a data collection unit, an analysis unit, a score calculation unit, and a feedback unit. The data collector records daily activities of the user. The analysis unit analyzes the data collected by the data collection unit and evaluates how much each behavior contributes to the SDGs based on a specific criterion. The score calculation unit calculates an SDGs score based on the analysis result obtained by the analysis unit. The feedback unit provides the score calculated by the score calculation unit to the user and indicates an improvement point.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technology has the problem of making it difficult to specifically evaluate the extent to which daily actions contribute to the SDGs.

[0005] The system according to the embodiment aims to specifically evaluate the extent to which a user's daily actions contribute to the SDGs and provide the result as a score. [Means for solving the problem]

[0006] The system according to the embodiment includes a data collection unit, an analysis unit, a score calculation unit, and a feedback unit. The data collection unit records the user's daily activities. The analysis unit analyzes the data collected by the data collection unit and evaluates the degree to which each activity contributes to the SDGs based on specific criteria. The score calculation unit calculates an SDG score based on the analysis results obtained by the analysis unit. The feedback unit provides the user with the score calculated by the score calculation unit and indicates areas for improvement. [Effects of the Invention]

[0007] The system according to the embodiment can specifically evaluate the extent to which a user's daily actions contribute to the SDGs and provide the result as a score. [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) The SDG scoring system according to an embodiment of the present invention records a user's daily activities, analyzes them using a generating AI, evaluates their contribution to the SDGs, and provides a score to the user. The SDG scoring system also allows users to check their SDG scores and identify areas for improvement. For example, the SDG scoring system records a user's daily activities, including their commute method, food choices, and energy consumption. This information is automatically collected via smartphones or wearable devices. The SDG scoring system then uses a generating AI to analyze the collected data and evaluate the degree to which each activity contributes to the SDGs. The input to the generating AI is the collected data itself, and the generating AI makes its evaluation based on that content. For example, the generating AI receives a prompt such as, "Please rate the degree to which this activity contributes to the SDGs," and evaluates the activity's contribution. The SDG scoring system then calculates an SDG score for each action based on the results of the generative AI. For example, if using public transportation reduces CO2 emissions, the action is evaluated as contributing to "climate change countermeasures." The SDG scoring system then provides the calculated score to the user and indicates areas for improvement. For example, using energy-efficient home appliances would be assigned a high score for "sustainable energy use." This allows users to consciously choose actions in their daily lives that contribute to the SDGs and contribute to the realization of a sustainable society. The SDG scoring system automatically records users' daily actions, evaluates their contribution to the SDGs, and generates a score. For example, users can specifically see how much their actions contribute to the SDGs and identify areas for improvement. Furthermore, users can consciously choose sustainable actions and contribute to the realization of a sustainable society.

[0029] The SDGs scoring system according to the embodiment includes a data collection unit, an analysis unit, a score calculation unit, and a feedback unit. The data collection unit records a user's daily behavior. The user's daily behavior may include, but is not limited to, commuting method, food choices, and energy consumption. The data collection unit automatically collects the user's daily behavior via, for example, a smartphone or a wearable device. The data collection unit may also collect data manually entered by the user. For example, the data collection unit may collect data entered by the user via a smartphone app. The analysis unit uses a generation AI to analyze the data collected by the data collection unit and evaluate the degree to which each behavior contributes to the SDGs. The evaluation is performed based on specific criteria, such as, but not limited to, energy consumption and waste reduction. For example, the generation AI uses a text generation AI (e.g., LLM) to analyze the collected data and evaluate the contribution of each behavior. The analysis unit may also use a multimodal generation AI to analyze the collected data. The analysis unit may also use the generation AI to quantify the contribution of each behavior. For example, text generation AI has learned large amounts of data and has advanced analytical capabilities. Multimodal generation AI can handle multiple modalities, including not only text but also images and audio. The generation AI uses data analysis technology to evaluate and quantify the contribution of each action. The score calculation unit calculates an SDG score for each action based on the analysis results obtained by the analysis unit. The score is calculated based on specific criteria, such as, but not limited to, a score range and evaluation items. For example, the score calculation unit assigns a score ranging from 0 to 100 based on the analysis results. The score calculation unit can also assign an evaluation ranging from A to F based on the analysis results. The score calculation unit can also calculate percentiles based on the analysis results. For example, the score calculation unit scales the score so that the higher the contribution, the higher the score. For example, the score calculation unit assigns an evaluation such that an A is given for a contribution of 90% or more and a B is given for a contribution of 80% or more.A percentile is an index indicating a relative position within a whole. For example, the score calculation unit gives a high score to behaviors that fall in the top 10%. The feedback unit provides the user with the score calculated by the score calculation unit and indicates areas for improvement. The feedback may be provided in the form of, for example, specific behavioral suggestions or guidelines for improvement, but is not limited to these examples. For example, the feedback unit makes specific behavioral suggestions to the user based on the score. The feedback unit can also provide the user with guidelines for improvement based on the score. The feedback unit can also provide an encouraging message to the user based on the score. For example, the feedback unit indicates areas for improvement if the score is low, and provides an encouraging message if the score is high. In this way, the SDGs scoring system according to the embodiment can automatically record the user's daily behavior, evaluate the user's contribution to the SDGs, and score the user. Some or all of the above-described processing in the feedback unit may be performed using, for example, AI, or may be performed without using AI. For example, the feedback unit can provide feedback using an AI model that inputs the score calculated by the score calculation unit and outputs feedback.

[0030] The SDG scoring system includes a data collection unit that automatically collects a user's daily activities through a specific smartphone or wearable device. The data collection unit automatically collects the user's daily activities through the smartphone or wearable device. Examples of smartphones and wearable devices include, but are not limited to, smartwatches and fitness trackers. The data collection unit records the user's steps and heart rate, for example, using a smartwatch. The data collection unit can also record the user's exercise volume and calorie consumption using a fitness tracker. The data collection unit can also record the user's movement route using the smartphone's GPS function. For example, the data collection unit records the user's steps using a smartwatch, the user's exercise volume using a fitness tracker, and the user's movement route using the smartphone's GPS function. This automatic data collection through the smartphone or wearable device reduces the burden on the user and enables accurate data to be obtained. Some or all of the above-described processing in the data collection unit may be performed using, for example, AI, or without AI. For example, the data collection unit can input data obtained from a smartwatch or fitness tracker into the generation AI and have the generation AI analyze the data.

[0031] The analysis unit can analyze the collected data and evaluate the degree to which each action contributes to the SDGs. For example, the analysis unit analyzes the collected data and evaluates the degree to which each action contributes to the SDGs. The evaluation is performed based on specific criteria, such as energy consumption and waste reduction. The analysis unit can also quantify the collected data and evaluate the contribution of each action. For example, the analysis unit can quantify energy consumption and assign a high score to energy-efficient actions. The analysis unit can also quantify waste reduction and assign a high score to actions that reduce waste. The analysis unit can also analyze the collected data and use an algorithm to evaluate the contribution of each action. For example, the analysis unit can use a machine learning algorithm to evaluate the contribution of each action. In this way, by analyzing the collected data and evaluating the contribution of each action to the SDGs, the user can grasp the specific degree of contribution. Some or all of the above-mentioned processing by the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the collected data into the generation AI and have the generation AI evaluate the contribution of each action.

[0032] The score calculation unit can calculate an SDG score for each action based on the analysis results. The score calculation unit, for example, calculates an SDG score for each action based on the analysis results. The score is calculated based on specific criteria, such as a score range and evaluation items. The score calculation unit can also quantify the score based on the analysis results to indicate the contribution of each action. For example, the score calculation unit assigns a score ranging from 0 to 100 based on the analysis results. The score calculation unit can also assign an evaluation from A to F based on the analysis results. The score calculation unit can also calculate a percentile based on the analysis results. For example, the score calculation unit scales the score so that the higher the contribution, the higher the score. For example, the score calculation unit assigns an A for a contribution of 90% or more and a B for a contribution of 80% or more. The percentile is an index that indicates a relative position within the whole. For example, the score calculation unit assigns a high evaluation to actions that fall in the top 10%. In this way, by calculating an SDG score based on the analysis results, it is possible to specifically indicate the extent to which a user is contributing to the SDGs. Some or all of the above-described processing in the score calculation unit may be performed using, for example, AI, or may be performed without using AI. For example, the score calculation unit may input the analysis results into the generation AI and have the generation AI calculate the SDG score.

[0033] The feedback unit can provide the user with a score and indicate areas for improvement. For example, the feedback unit can provide the user with the score calculated by the score calculation unit and indicate areas for improvement. The feedback can be provided in the form of, for example, specific behavioral suggestions or guidelines for improvement. The feedback unit can also make specific behavioral suggestions to the user based on the score. For example, the feedback unit can indicate areas for improvement if the score is low, and provide an encouraging message if the score is high. The feedback unit can also provide guidelines for improvement to the user based on the score. For example, the feedback unit can provide advice such as, "Using energy-efficient home appliances will result in a high score for "sustainable energy use." The feedback unit can also provide an encouraging message to the user based on the score. For example, if the score is high, the feedback unit can provide a message such as, "Great! Keep it up!". By providing the user with a score and indicating areas for improvement, the user can consciously choose sustainable behavior. Some or all of the above-described processing in the feedback unit can be performed using, for example, AI, or without AI. For example, the feedback unit can provide feedback using an AI model that receives the score calculated by the score calculation unit as input and outputs feedback.

[0034] The data collection unit can analyze the user's past behavioral history and select the optimal data collection method. The data collection unit, for example, analyzes the user's past behavioral history and selects the optimal data collection method. The past behavioral history includes, for example, the frequency and type of behavior. The data collection unit can also adjust the timing of data collection based on the past behavioral history. For example, the data collection unit prioritizes data collection of behaviors that the user frequently performed in the past. The data collection unit can also analyze the user's past behavioral patterns and determine the optimal data collection timing. The data collection unit can also customize the data collection method for a specific behavior based on the user's past behavioral history. For example, the data collection unit selects the optimal data collection method for a specific behavior based on the user's past behavioral history. In this way, by analyzing the user's past behavioral history, the optimal data collection method can be selected and data can be collected efficiently. 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 AI. For example, the data collection unit can input the user's past behavioral history into a generation AI and cause the generation AI to select the optimal data collection method.

[0035] The data collection unit can filter data based on the user's current lifestyle and areas of interest when collecting data. For example, the data collection unit filters data based on the user's current lifestyle and areas of interest when collecting data. The current lifestyle includes, for example, a lifestyle pattern and daily routine. The areas of interest include, for example, hobbies and topics of interest. The data collection unit, for example, prioritizes collecting data related to the user's current areas of interest. The data collection unit can also adjust the content of data collection based on the user's lifestyle (e.g., at work, on vacation). The data collection unit can also adjust the timing of data collection based on the user's current activity (e.g., exercising, reading). For example, the data collection unit prioritizes collecting data related to the user's current areas of interest, adjusts the content of data collection based on the user's lifestyle, and adjusts the timing of data collection based on the user's current activity. In this way, highly relevant data can be collected by filtering data based on the user's current lifestyle 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 input data about the user's current living situation and areas of interest into the generation AI and have the generation AI filter the data.

[0036] The data collection unit can select the optimal collection means depending on the user's input method when collecting data. For example, the data collection unit selects the optimal collection means depending on the user's input method (voice, text, image, etc.) when collecting data. Input methods include, for example, voice input, text input, and image input. For example, when the user is using voice input, the data collection unit prioritizes collecting voice data. Also, when the user is using text input, the data collection unit can prioritize collecting text data. Also, when the user is using image input, the data collection unit can prioritize collecting image data. For example, when the user is using voice input, the data collection unit prioritizes collecting voice data; when the user is using text input, the data collection unit prioritizes collecting text data; and when the user is using image input, the data collection unit prioritizes collecting image data. This allows efficient data collection by selecting the optimal collection means depending on the user's input method. 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 may input data regarding the user's input method to the generation AI and cause the generation AI to select the optimal collection means.

[0037] The data collection unit can prioritize collecting highly relevant data by taking into consideration the user's geographical location information when collecting data. For example, the data collection unit prioritizes collecting highly relevant data by taking into consideration the user's geographical location information when collecting data. Geographical location information includes, for example, GPS data and the accuracy of location information. For example, when the user is in a specific area, the data collection unit prioritizes collecting data related to that area. Furthermore, when the user is traveling, the data collection unit can prioritize collecting data related to the travel destination. Furthermore, when the user is at home, the data collection unit can prioritize collecting data related to the home. For example, when the user is in a specific area, the data collection unit prioritizes collecting data related to the area; when the user is traveling, the data collection unit prioritizes collecting data related to the travel destination; and when the user is at home, the data collection unit prioritizes collecting data related to the home. This allows for efficient data collection by collecting highly relevant data by taking into consideration 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 input the user's geographical location information into the generation AI and cause the generation AI to collect highly relevant data.

[0038] The data collection unit can analyze the user's social media activity and collect related data during data collection. For example, the data collection unit can analyze the user's social media activity and collect related data during data collection. Social media activity includes, for example, the content of posts and the number of likes. For example, the data collection unit can collect data related to places where the user has checked in on social media. The data collection unit can also analyze the content of the user's social media posts and collect related data. The data collection unit can also collect related data by referring to the activities of the user's friends on social media. For example, the data collection unit collects data related to places where the user has checked in on social media, analyzes the content of the user's social media posts, collects related data, and collects related data by referring to the activities of the user's friends on social media. In this way, highly relevant data can be collected by analyzing the user's social media activity. Some or all of the above-described processing in the data collection unit can be performed using, for example, AI, or without AI. For example, the data collection unit can input data related to the user's social media activity into the generation AI and cause the generation AI to collect related data.

[0039] 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. The past feedback includes, for example, the type of feedback and the frequency of reflection. The data collection unit adjusts the data collection method, for example, based on feedback provided by the user in the past. The data collection unit can also prioritize the use of a specific data collection means based on the user's past feedback. The data collection unit can also adjust the timing of data collection by referring to the user's past feedback. For example, the data collection unit adjusts the data collection method based on the user's past feedback, prioritizes the use of a specific data collection means based on the user's past feedback, and adjusts the timing of data collection by referring to the user's past feedback. In this way, the collection method can be customized by reflecting the user's past feedback, and data can be collected efficiently. 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 input data regarding the user's past feedback to the generation AI and cause the generation AI to customize the collection method.

[0040] The analysis unit can adjust the level of detail of the analysis based on the importance of the behavior during analysis. For example, the analysis unit adjusts the level of detail of the analysis based on the importance of the behavior during analysis. The importance of the behavior includes, for example, the impact of the behavior and the frequency of the behavior. For example, the analysis unit performs a detailed analysis on a highly important behavior. The analysis unit can also perform a simplified analysis on a less important behavior. The analysis unit can also determine the priority of the analysis based on the importance of the behavior. For example, the analysis unit performs a detailed analysis on a highly important behavior and a simplified analysis on a less important behavior, and determines the priority of the analysis based on the importance of the behavior. In this way, the analysis can be performed efficiently by adjusting the level of detail of the analysis based on the importance of the behavior. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input data regarding the importance of the behavior to the generation AI and cause the generation AI to adjust the level of detail of the analysis.

[0041] The analysis unit can apply different analysis algorithms depending on the category of the behavior during analysis. For example, the analysis unit applies different analysis algorithms depending on the category of the behavior during analysis. Behavior categories include, for example, environmental protection and social contribution. For example, the analysis unit applies an environmental impact assessment algorithm to behavior related to environmental protection. The analysis unit can also apply a health impact assessment algorithm to behavior related to health. The analysis unit can also apply a social impact assessment algorithm to behavior related to social contribution. For example, the analysis unit applies an environmental impact assessment algorithm to behavior related to environmental protection, a health impact assessment algorithm to behavior related to health, and a social impact assessment algorithm to behavior related to social contribution. In this way, by applying different analysis algorithms depending on the category of the behavior, more accurate analysis results can be provided. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input data regarding the category of the behavior into the generation AI and cause the generation AI to apply different analysis algorithms.

[0042] 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, past data and a method for saving the analysis results. For example, the analysis unit can adjust the analysis algorithm based on the user's past analysis results. The analysis unit can also improve the analysis accuracy for specific behaviors based on the user's past analysis results. The analysis unit can also determine the priority of the analysis by referring to the user's past analysis results. For example, the analysis unit can adjust the analysis algorithm based on the user's past analysis results, improve the analysis accuracy for specific behaviors based on the user's past analysis results, and determine the priority of the analysis by referring to the user's past analysis results. This allows the accuracy of the analysis to be improved by referring to the user's past analysis results. Some or all of the above-described processing in the analysis unit can be performed using, for example, AI, or without AI. For example, the analysis unit can input data regarding the user's past analysis results into the generation AI and cause the generation AI to improve the accuracy of the analysis.

[0043] The analysis unit can determine the priority of analysis based on the time when the behavior was performed during analysis. The analysis unit, for example, determines the priority of analysis based on the time when the behavior was performed during analysis. The time when the behavior was performed includes, for example, the frequency and timing of the behavior. The analysis unit, for example, prioritizes analysis of recently performed behavior. The analysis unit can also postpone analysis of previously performed behavior. The analysis unit can also adjust the level of detail of the analysis depending on the time when the behavior was performed. For example, the analysis unit prioritizes analysis of recently performed behavior and postpones analysis of previously performed behavior, adjusting the level of detail of the analysis depending on the time when the behavior was performed. In this way, determining the priority of analysis based on the time when the behavior was performed enables efficient analysis. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without AI. For example, the analysis unit can input data regarding the time when the behavior was performed to the generation AI and cause the generation AI to determine the analysis priority.

[0044] The analysis unit can adjust the order of analysis based on the relevance of actions during analysis. The analysis unit, for example, adjusts the order of analysis based on the relevance of actions during analysis. The relevance of actions includes, for example, the interrelationship between actions and the influence of actions. The analysis unit, for example, prioritizes analysis of highly relevant actions. The analysis unit can also postpone analysis of less relevant actions. The analysis unit can also adjust the level of detail of the analysis according to the relevance of actions. For example, the analysis unit prioritizes analysis of highly relevant actions and postpones analysis of less relevant actions, adjusting the level of detail of the analysis according to the relevance of actions. In this way, adjusting the order of analysis based on the relevance of actions enables efficient analysis. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without AI. For example, the analysis unit can input data regarding the relevance of actions to the generation AI and cause the generation AI to adjust the order of analysis.

[0045] 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 and educational background. 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 analysis results in simple language. Furthermore, the analysis unit can adjust the level of detail of the analysis results according to the user's level of expertise. For example, if the user has technical expertise, the analysis unit can provide analysis results that use a lot of technical terms, and if the user does not have technical expertise, the analysis unit can provide analysis results in simple language, adjusting the level of detail of the analysis results according to the user's level of expertise. In this way, by adjusting the use of technical terms in the analysis according to the user's level of expertise, analysis results that are easy for the user to understand can be provided. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without AI. For example, the analysis unit can input data regarding the user's level of expertise to the generation AI and cause the generation AI to adjust the use of technical terms in the analysis.

[0046] The score calculation unit can adjust the level of detail of the score based on the importance of the action when calculating the score. For example, the score calculation unit adjusts the level of detail of the score based on the importance of the action when calculating the score. The importance of the action includes, for example, the influence of the action and the frequency of the action. For example, the score calculation unit calculates a detailed score for an action with high importance. The score calculation unit can also calculate a simplified score for an action with low importance. The score calculation unit can also determine the priority of the scores according to the importance of the action. For example, the score calculation unit calculates a detailed score for an action with high importance and a simplified score for an action with low importance, and determines the priority of the scores according to the importance of the action. In this way, the score can be calculated efficiently by adjusting the level of detail of the score based on the importance of the action. Some or all of the above-described processing in the score calculation unit may be performed using, for example, AI, or may be performed without using AI. For example, the score calculation unit can input data regarding the importance of the action to a generation AI and cause the generation AI to adjust the level of detail of the score.

[0047] The score calculation unit can apply different score calculation algorithms depending on the category of the behavior when calculating the score. For example, the score calculation unit applies different score calculation algorithms depending on the category of the behavior when calculating the score. The categories of the behavior include, for example, environmental protection and social contribution. For example, the score calculation unit applies an environmental impact assessment algorithm to behavior related to environmental protection. The score calculation unit can also apply a health impact assessment algorithm to behavior related to health. The score calculation unit can also apply a social impact assessment algorithm to behavior related to social contribution. For example, the score calculation unit applies an environmental impact assessment algorithm to behavior related to environmental protection, a health impact assessment algorithm to behavior related to health, and a social impact assessment algorithm to behavior related to social contribution. In this way, by applying different score calculation algorithms depending on the category of the behavior, more accurate scores can be provided. Some or all of the above-mentioned processing in the score calculation unit may be performed using, for example, AI, or may be performed without using AI. For example, the score calculation unit can input data regarding the category of the behavior to the generation AI and cause the generation AI to apply different score calculation algorithms.

[0048] The score calculation unit can improve the accuracy of the score by referring to the user's past score results when calculating the score. For example, the score calculation unit improves the accuracy of the score by referring to the user's past score results when calculating the score. Past score results include, for example, past data and a method for saving score results. For example, the score calculation unit adjusts the score calculation algorithm based on the user's past score results. The score calculation unit can also improve the score accuracy for a specific behavior based on the user's past score results. The score calculation unit can also determine the priority of the scores by referring to the user's past score results. For example, the score calculation unit adjusts the score calculation algorithm based on the user's past score results to improve the score accuracy for a specific behavior based on the user's past score results, and determines the priority of the scores by referring to the user's past score results. In this way, the accuracy of the score can be improved by referring to the user's past score results. Some or all of the above-described processing in the score calculation unit may be performed using, for example, AI, or may be performed without using AI. For example, the score calculation unit can input data regarding the user's past score results into the generation AI and cause the generation AI to improve the accuracy of the score.

[0049] The score calculation unit can determine the priority of scores based on the time when the actions were performed when calculating the scores. For example, the score calculation unit determines the priority of scores based on the time when the actions were performed when calculating the scores. The time when the actions were performed includes, for example, the frequency and timing of the actions. For example, the score calculation unit prioritizes calculating scores for recently performed actions. The score calculation unit can also postpone calculating scores for actions performed in the past. The score calculation unit can also adjust the level of detail of the scores depending on the time when the actions were performed. For example, the score calculation unit prioritizes calculating scores for recently performed actions and postpones calculating scores for actions performed in the past, and adjusts the level of detail of the scores depending on the time when the actions were performed. In this way, by determining the priority of scores based on the time when the actions were performed, scores can be calculated efficiently. Some or all of the above-described processing in the score calculation unit may be performed using, for example, AI, or may be performed without using AI. For example, the score calculation unit can input data regarding the time when the actions were performed to a generation AI and cause the generation AI to determine the priority of the scores.

[0050] The score calculation unit can adjust the order of scores based on the relevance of actions when calculating the scores. For example, the score calculation unit adjusts the order of scores based on the relevance of actions when calculating the scores. The relevance of actions includes, for example, the interrelationship between actions and the influence of actions. For example, the score calculation unit prioritizes calculating scores for highly relevant actions. The score calculation unit can also postpone calculating scores for less relevant actions. The score calculation unit can also adjust the level of detail of the scores according to the relevance of actions. For example, the score calculation unit prioritizes calculating scores for highly relevant actions and postpones calculating scores for less relevant actions, and adjusts the level of detail of the scores according to the relevance of actions. In this way, adjusting the order of scores based on the relevance of actions enables efficient score calculation. Some or all of the above-described processing in the score calculation unit may be performed using, for example, AI, or may be performed without using AI. For example, the score calculation unit can input data regarding the relevance of actions to a generation AI and cause the generation AI to adjust the order of the scores.

[0051] The score calculation unit can adjust the use of technical terms in the score according to the user's level of expertise when calculating the score. For example, the score calculation unit adjusts the use of technical terms in the score according to the user's level of expertise when calculating the score. The level of expertise includes, for example, the user's occupation and educational background. For example, if the user has technical expertise, the score calculation unit provides a score that uses a lot of technical terms. Furthermore, if the user does not have technical expertise, the score calculation unit can provide a score in simple terms. Furthermore, the score calculation unit can adjust the level of detail of the score according to the user's level of expertise. For example, if the user has technical expertise, the score calculation unit provides a score that uses a lot of technical terms, and if the user does not have technical expertise, the score calculation unit provides a score in simple terms, adjusting the level of detail of the score according to the user's level of expertise. In this way, by adjusting the use of technical terms in the score according to the user's level of expertise, it is possible to provide a score that is easy for the user to understand. Some or all of the above-mentioned processing in the score calculation unit may be performed, for example, using AI or without AI. For example, the score calculation unit can input data regarding the user's level of expertise into the generation AI and cause the generation AI to adjust the use of terminology in the score.

[0052] The feedback unit can adjust the level of detail of the feedback based on the importance of the behavior when providing feedback. For example, the feedback unit adjusts the level of detail of the feedback based on the importance of the behavior when providing feedback. The importance of the behavior includes, for example, the impact of the behavior and the frequency of the behavior. For example, the feedback unit provides detailed feedback for a highly important behavior. The feedback unit can also provide simplified feedback for a less important behavior. The feedback unit can also determine the priority of the feedback based on the importance of the behavior. For example, the feedback unit provides detailed feedback for a highly important behavior and simplified feedback for a less important behavior, and determines the priority of the feedback based on the importance of the behavior. In this way, by adjusting the level of detail of the feedback based on the importance of the behavior, it is possible to provide feedback efficiently. Some or all of the above-described processing in the feedback unit may be performed using, for example, AI, or may be performed without using AI. For example, the feedback unit may input data regarding the importance of the behavior to the generation AI and cause the generation AI to adjust the level of detail of the feedback.

[0053] The feedback unit can apply different feedback algorithms depending on the category of the behavior when providing feedback. For example, the feedback unit applies different feedback algorithms depending on the category of the behavior when providing feedback. The categories of behavior include, for example, environmental protection and social contribution. For example, the feedback unit applies an environmental impact assessment algorithm to behavior related to environmental protection. The feedback unit can also apply a health impact assessment algorithm to behavior related to health. The feedback unit can also apply a social impact assessment algorithm to behavior related to social contribution. For example, the feedback unit applies an environmental impact assessment algorithm to behavior related to environmental protection, a health impact assessment algorithm to behavior related to health, and a social impact assessment algorithm to behavior related to social contribution. In this way, by applying different feedback algorithms depending on the category of the behavior, more accurate feedback can be provided. Some or all of the above-described processing in the feedback unit may be performed using, for example, AI, or may be performed without using AI. For example, the feedback unit can input data regarding the category of the behavior into the generation AI and cause the generation AI to apply different feedback algorithms.

[0054] The feedback unit can improve the accuracy of the feedback by referring to the user's past feedback results when providing feedback. For example, the feedback unit can improve the accuracy of the feedback by referring to the user's past feedback results when providing feedback. Past feedback results include, for example, past data and a method for saving the feedback results. For example, the feedback unit can adjust a feedback algorithm based on the user's past feedback results. The feedback unit can also improve the feedback accuracy for a specific behavior based on the user's past feedback results. The feedback unit can also determine the priority of the feedback by referring to the user's past feedback results. For example, the feedback unit can adjust a feedback algorithm based on the user's past feedback results, improve the feedback accuracy for a specific behavior based on the user's past feedback results, and determine the priority of the feedback by referring to the user's past feedback results. In this way, the accuracy of the feedback can be improved by referring to the user's past feedback results. Some or all of the above-described processing in the feedback unit may be performed using, for example, AI, or may be performed without using AI. For example, the feedback unit can input data regarding the user's past feedback results to the generation AI and cause the generation AI to improve the accuracy of the feedback.

[0055] The feedback unit can determine the priority of feedback based on the time when the action was performed when providing feedback. For example, the feedback unit determines the priority of feedback based on the time when the action was performed when providing feedback. The time when the action was performed includes, for example, the frequency of the action and the timing of the action. For example, the feedback unit prioritizes providing feedback for recently performed actions. The feedback unit can also provide feedback later for previously performed actions. The feedback unit can also adjust the level of detail of the feedback depending on the time when the action was performed. For example, the feedback unit prioritizes providing feedback for recently performed actions and provides feedback later for previously performed actions, adjusting the level of detail of the feedback depending on the time when the action was performed. In this way, by determining the priority of feedback based on the time when the action was performed, it is possible to provide feedback efficiently. Some or all of the above-described processing in the feedback unit may be performed using, for example, AI, or may be performed without using AI. For example, the feedback unit can input data regarding the time when the action was performed to the generation AI and cause the generation AI to determine the priority of the feedback.

[0056] The feedback unit can adjust the order of feedback based on the relevance of actions when providing feedback. For example, the feedback unit adjusts the order of feedback based on the relevance of actions when providing feedback. The relevance of actions includes, for example, the interrelationship between actions and the influence of actions. For example, the feedback unit provides feedback preferentially to highly relevant actions. The feedback unit can also provide feedback later to less relevant actions. The feedback unit can also adjust the level of detail of the feedback depending on the relevance of actions. For example, the feedback unit provides feedback preferentially to highly relevant actions and provides feedback later to less relevant actions, adjusting the level of detail of the feedback depending on the relevance of actions. In this way, by adjusting the order of feedback based on the relevance of actions, it is possible to provide feedback efficiently. Some or all of the above-described processing in the feedback unit may be performed using, for example, AI, or may be performed without using AI. For example, the feedback unit can input data regarding the relevance of actions to a generation AI and cause the generation AI to adjust the order of feedback.

[0057] The feedback unit may adjust the use of technical terms in the feedback according to the user's level of expertise when providing feedback. For example, the feedback unit may adjust the use of technical terms in the feedback according to the user's level of expertise when providing feedback. Examples of the level of expertise include the user's occupation and educational background. For example, the feedback unit may provide jargon-heavy feedback when the user has specialized knowledge. Furthermore, the feedback unit may provide feedback in simple language when the user does not have specialized knowledge. Furthermore, the feedback unit may adjust the level of detail of the feedback according to the user's level of expertise. For example, the feedback unit may provide jargon-heavy feedback when the user has specialized knowledge, and provide feedback in simple language when the user does not have specialized knowledge, adjusting the level of detail of the feedback according to the user's level of expertise. This allows for the provision of feedback that is easy for the user to understand by adjusting the use of technical terms in the feedback according to the user's level of expertise. Some or all of the above-described processing in the feedback unit may be performed using, for example, AI, or may be performed without AI. For example, the feedback unit may input data regarding the user's level of expertise to the generation AI and cause the generation AI to adjust the use of technical terms in the feedback.

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

[0059] When collecting user behavioral data, the data collection unit can monitor the user's health condition and adjust the frequency and method of data collection based on the user's health condition. For example, if the user is tired, the frequency of data collection can be reduced to reduce the burden on the user. Also, if the user is in good health, the frequency of data collection can be increased to collect more detailed data. Furthermore, if the user is ill or injured, data collection can be temporarily stopped and resumed after recovery. In this way, adjusting the data collection method according to the user's health condition can reduce the burden on the user and collect data at an appropriate time.

[0060] When analyzing the user's behavioral data, the analysis unit can take the user's lifestyle into consideration and adjust the timing and method of analysis based on the lifestyle. For example, if the user is a nocturnal person, the analysis unit can analyze the data at night and provide feedback during the time period when the user is active. Alternatively, if the user is a morning person, the analysis unit can analyze the data in the morning and provide feedback before the user starts their activities. Furthermore, if the user's lifestyle is irregular, the timing of data analysis can be flexibly adjusted to provide feedback tailored to the user's activities. In this way, by adjusting the timing and method of analysis according to the user's lifestyle, feedback can be provided at the optimal time for the user.

[0061] When scoring the user's behavioral data, the score calculation unit can take into account the user's individual goals and values ​​and adjust the score calculation method based on the individual goals and values. For example, if the user values ​​environmental protection, a high score can be assigned to behavior related to environmental protection. Also, if the user values ​​social contribution, a high score can be assigned to behavior related to social contribution. Furthermore, if the user values ​​health, a high score can be assigned to health-related behavior. In this way, by adjusting the score calculation method according to the user's individual goals and values, it is possible to provide a score that is meaningful to the user.

[0062] When providing feedback to a user, the feedback unit can take the user's learning style into consideration and adjust the form and content of the feedback based on the learning style. For example, if the user has a visual learning style, visual feedback using graphs and charts can be provided. If the user has an auditory learning style, feedback using audio messages can be provided. Furthermore, if the user has an experiential learning style, a specific action plan for learning through actual actions can be provided. In this way, by adjusting the form and content of feedback according to the user's learning style, it is possible to provide effective feedback to the user.

[0063] The data collection unit may have a function for protecting the user's privacy when collecting user behavior data. For example, the data collection unit may collect data with the user's consent and anonymize the collected data. The data collection unit may also provide a function that allows the user to set the scope and frequency of data collection. Furthermore, the data collection unit may also provide a function that allows the user to check the collected data and delete it as necessary. This allows the data to be collected while protecting the user's privacy, thereby gaining the user's trust.

[0064] The processing flow of the first embodiment will be briefly explained below.

[0065] Step 1: The data collection unit records the user's daily behavior. The user's daily behavior includes commuting method, food choices, energy consumption, etc. The data collection unit automatically collects the user's daily behavior via smartphones and wearable devices. It can also collect data manually entered by the user. Step 2: The analysis unit uses the generation AI to analyze the data collected by the data collection unit and evaluate the degree to which each action contributes to the SDGs. The evaluation is based on specific criteria such as energy consumption and waste reduction. The generation AI uses text generation AI and multimodal generation AI to analyze the collected data and quantify the contribution of each action. Step 3: The score calculation unit calculates an SDG score for each behavior based on the analysis results obtained by the analysis unit. The score is calculated based on specific criteria such as a score range and evaluation items. For example, a score can be assigned on a scale of 0 to 100, a grade of A to F, or percentiles can be calculated. Step 4: The feedback unit provides the user with the score calculated by the score calculation unit and suggests areas for improvement. The feedback is provided in the form of specific suggestions for action or guidelines for improvement. For example, if the score is low, the feedback unit suggests areas for improvement, and if the score is high, the feedback unit provides an encouraging message.

[0066] (Example 2) The SDG scoring system according to an embodiment of the present invention records a user's daily activities, analyzes them using a generating AI, evaluates their contribution to the SDGs, and provides a score to the user. The SDG scoring system also allows users to check their SDG scores and identify areas for improvement. For example, the SDG scoring system records a user's daily activities, including their commute method, food choices, and energy consumption. This information is automatically collected via smartphones or wearable devices. The SDG scoring system then uses a generating AI to analyze the collected data and evaluate the degree to which each activity contributes to the SDGs. The input to the generating AI is the collected data itself, and the generating AI makes its evaluation based on that content. For example, the generating AI receives a prompt such as, "Please rate the degree to which this activity contributes to the SDGs," and evaluates the activity's contribution. The SDG scoring system then calculates an SDG score for each action based on the results of the generative AI. For example, if using public transportation reduces CO2 emissions, the action is evaluated as contributing to "climate change countermeasures." The SDG scoring system then provides the calculated score to the user and indicates areas for improvement. For example, using energy-efficient home appliances would be assigned a high score for "sustainable energy use." This allows users to consciously choose actions in their daily lives that contribute to the SDGs and contribute to the realization of a sustainable society. The SDG scoring system automatically records users' daily actions, evaluates their contribution to the SDGs, and generates a score. For example, users can specifically see how much their actions contribute to the SDGs and identify areas for improvement. Furthermore, users can consciously choose sustainable actions and contribute to the realization of a sustainable society.

[0067] The SDGs scoring system according to the embodiment includes a data collection unit, an analysis unit, a score calculation unit, and a feedback unit. The data collection unit records a user's daily behavior. The user's daily behavior may include, but is not limited to, commuting method, food choices, and energy consumption. The data collection unit automatically collects the user's daily behavior via, for example, a smartphone or a wearable device. The data collection unit may also collect data manually entered by the user. For example, the data collection unit may collect data entered by the user via a smartphone app. The analysis unit uses a generation AI to analyze the data collected by the data collection unit and evaluate the degree to which each behavior contributes to the SDGs. The evaluation is performed based on specific criteria, such as, but not limited to, energy consumption and waste reduction. For example, the generation AI uses a text generation AI (e.g., LLM) to analyze the collected data and evaluate the contribution of each behavior. The analysis unit may also use a multimodal generation AI to analyze the collected data. The analysis unit may also use the generation AI to quantify the contribution of each behavior. For example, text generation AI has learned large amounts of data and has advanced analytical capabilities. Multimodal generation AI can handle multiple modalities, including not only text but also images and audio. The generation AI uses data analysis technology to evaluate and quantify the contribution of each action. The score calculation unit calculates an SDG score for each action based on the analysis results obtained by the analysis unit. The score is calculated based on specific criteria, such as, but not limited to, a score range and evaluation items. For example, the score calculation unit assigns a score ranging from 0 to 100 based on the analysis results. The score calculation unit can also assign an evaluation ranging from A to F based on the analysis results. The score calculation unit can also calculate percentiles based on the analysis results. For example, the score calculation unit scales the score so that the higher the contribution, the higher the score. For example, the score calculation unit assigns an evaluation such that an A is given for a contribution of 90% or more and a B is given for a contribution of 80% or more.A percentile is an index indicating a relative position within a whole. For example, the score calculation unit gives a high score to behaviors that fall in the top 10%. The feedback unit provides the user with the score calculated by the score calculation unit and indicates areas for improvement. The feedback may be provided in the form of, for example, specific behavioral suggestions or guidelines for improvement, but is not limited to these examples. For example, the feedback unit makes specific behavioral suggestions to the user based on the score. The feedback unit can also provide the user with guidelines for improvement based on the score. The feedback unit can also provide an encouraging message to the user based on the score. For example, the feedback unit indicates areas for improvement if the score is low, and provides an encouraging message if the score is high. In this way, the SDGs scoring system according to the embodiment can automatically record the user's daily behavior, evaluate the user's contribution to the SDGs, and score the user. Some or all of the above-described processing in the feedback unit may be performed using, for example, AI, or may be performed without using AI. For example, the feedback unit can provide feedback using an AI model that inputs the score calculated by the score calculation unit and outputs feedback.

[0068] The SDG scoring system includes a data collection unit that automatically collects a user's daily activities through a specific smartphone or wearable device. The data collection unit automatically collects the user's daily activities through the smartphone or wearable device. Examples of smartphones and wearable devices include, but are not limited to, smartwatches and fitness trackers. The data collection unit records the user's steps and heart rate, for example, using a smartwatch. The data collection unit can also record the user's exercise volume and calorie consumption using a fitness tracker. The data collection unit can also record the user's movement route using the smartphone's GPS function. For example, the data collection unit records the user's steps using a smartwatch, the user's exercise volume using a fitness tracker, and the user's movement route using the smartphone's GPS function. This automatic data collection through the smartphone or wearable device reduces the burden on the user and enables accurate data to be obtained. Some or all of the above-described processing in the data collection unit may be performed using, for example, AI, or without AI. For example, the data collection unit can input data obtained from a smartwatch or fitness tracker into the generation AI and have the generation AI analyze the data.

[0069] The analysis unit can analyze the collected data and evaluate the degree to which each action contributes to the SDGs. For example, the analysis unit analyzes the collected data and evaluates the degree to which each action contributes to the SDGs. The evaluation is performed based on specific criteria, such as energy consumption and waste reduction. The analysis unit can also quantify the collected data and evaluate the contribution of each action. For example, the analysis unit can quantify energy consumption and assign a high score to energy-efficient actions. The analysis unit can also quantify waste reduction and assign a high score to actions that reduce waste. The analysis unit can also analyze the collected data and use an algorithm to evaluate the contribution of each action. For example, the analysis unit can use a machine learning algorithm to evaluate the contribution of each action. In this way, by analyzing the collected data and evaluating the contribution of each action to the SDGs, the user can grasp the specific degree of contribution. Some or all of the above-mentioned processing by the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the collected data into the generation AI and have the generation AI evaluate the contribution of each action.

[0070] The score calculation unit can calculate an SDG score for each action based on the analysis results. The score calculation unit, for example, calculates an SDG score for each action based on the analysis results. The score is calculated based on specific criteria, such as a score range and evaluation items. The score calculation unit can also quantify the score based on the analysis results to indicate the contribution of each action. For example, the score calculation unit assigns a score ranging from 0 to 100 based on the analysis results. The score calculation unit can also assign an evaluation from A to F based on the analysis results. The score calculation unit can also calculate a percentile based on the analysis results. For example, the score calculation unit scales the score so that the higher the contribution, the higher the score. For example, the score calculation unit assigns an A for a contribution of 90% or more and a B for a contribution of 80% or more. The percentile is an index that indicates a relative position within the whole. For example, the score calculation unit assigns a high evaluation to actions that fall in the top 10%. In this way, by calculating an SDG score based on the analysis results, it is possible to specifically indicate the extent to which a user is contributing to the SDGs. Some or all of the above-described processing in the score calculation unit may be performed using, for example, AI, or may be performed without using AI. For example, the score calculation unit may input the analysis results into the generation AI and have the generation AI calculate the SDG score.

[0071] The feedback unit can provide the user with a score and indicate areas for improvement. For example, the feedback unit can provide the user with the score calculated by the score calculation unit and indicate areas for improvement. The feedback can be provided in the form of, for example, specific behavioral suggestions or guidelines for improvement. The feedback unit can also make specific behavioral suggestions to the user based on the score. For example, the feedback unit can indicate areas for improvement if the score is low, and provide an encouraging message if the score is high. The feedback unit can also provide guidelines for improvement to the user based on the score. For example, the feedback unit can provide advice such as, "Using energy-efficient home appliances will result in a high score for "sustainable energy use." The feedback unit can also provide an encouraging message to the user based on the score. For example, if the score is high, the feedback unit can provide a message such as, "Great! Keep it up!". By providing the user with a score and indicating areas for improvement, the user can consciously choose sustainable behavior. Some or all of the above-described processing in the feedback unit can be performed using, for example, AI, or without AI. For example, the feedback unit can provide feedback using an AI model that receives the score calculated by the score calculation unit as input and outputs feedback.

[0072] The SDG scoring system includes a data collection unit that estimates a user's emotions using a specific method and adjusts the timing of data collection based on the estimated user emotions. The data collection unit estimates the user's emotions and adjusts the timing of data collection based on the estimated user emotions. Emotion estimation is performed using specific methods, such as facial expression recognition and voice analysis. For example, the data collection unit captures the user's facial expressions with a camera and estimates the emotion using a facial expression recognition algorithm. The data collection unit can also record the user's voice and estimate the emotion using voice analysis technology. For example, the data collection unit analyzes the tone and speed of the user's voice to estimate the emotion. The data collection unit adjusts the timing of data collection based on the estimated emotion. For example, if the user is feeling stressed, the frequency of data collection can be reduced to reduce the user's burden. Alternatively, if the user is relaxed, the frequency of data collection can be increased to collect more detailed data. Alternatively, if the user is in a hurry, data collection can be temporarily stopped and resumed later. This allows the timing of data collection to be adjusted according to the user's emotions, reducing the user's burden and collecting data at the appropriate time. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the data collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the data collection unit may input image data of a user captured by a camera into the generation AI and cause the generation AI to estimate the user's emotions.

[0073] The data collection unit can analyze the user's past behavioral history and select the optimal data collection method. The data collection unit, for example, analyzes the user's past behavioral history and selects the optimal data collection method. The past behavioral history includes, for example, the frequency and type of behavior. The data collection unit can also adjust the timing of data collection based on the past behavioral history. For example, the data collection unit prioritizes data collection of behaviors that the user frequently performed in the past. The data collection unit can also analyze the user's past behavioral patterns and determine the optimal data collection timing. The data collection unit can also customize the data collection method for a specific behavior based on the user's past behavioral history. For example, the data collection unit selects the optimal data collection method for a specific behavior based on the user's past behavioral history. In this way, by analyzing the user's past behavioral history, the optimal data collection method can be selected and data can be collected efficiently. 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 AI. For example, the data collection unit can input the user's past behavioral history into a generation AI and cause the generation AI to select the optimal data collection method.

[0074] The data collection unit can filter data based on the user's current lifestyle and areas of interest when collecting data. For example, the data collection unit filters data based on the user's current lifestyle and areas of interest when collecting data. The current lifestyle includes, for example, a lifestyle pattern and daily routine. The areas of interest include, for example, hobbies and topics of interest. The data collection unit, for example, prioritizes collecting data related to the user's current areas of interest. The data collection unit can also adjust the content of data collection based on the user's lifestyle (e.g., at work, on vacation). The data collection unit can also adjust the timing of data collection based on the user's current activity (e.g., exercising, reading). For example, the data collection unit prioritizes collecting data related to the user's current areas of interest, adjusts the content of data collection based on the user's lifestyle, and adjusts the timing of data collection based on the user's current activity. In this way, highly relevant data can be collected by filtering data based on the user's current lifestyle 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 input data about the user's current living situation and areas of interest into the generation AI and have the generation AI filter the data.

[0075] The data collection unit can select the optimal collection means depending on the user's input method when collecting data. For example, the data collection unit selects the optimal collection means depending on the user's input method (voice, text, image, etc.) when collecting data. Input methods include, for example, voice input, text input, and image input. For example, when the user is using voice input, the data collection unit prioritizes collecting voice data. Also, when the user is using text input, the data collection unit can prioritize collecting text data. Also, when the user is using image input, the data collection unit can prioritize collecting image data. For example, when the user is using voice input, the data collection unit prioritizes collecting voice data; when the user is using text input, the data collection unit prioritizes collecting text data; and when the user is using image input, the data collection unit prioritizes collecting image data. This allows efficient data collection by selecting the optimal collection means depending on the user's input method. 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 may input data regarding the user's input method to the generation AI and cause the generation AI to select the optimal collection means.

[0076] 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 using specific methods such as facial expression recognition and voice analysis. For example, the data collection unit can capture the user's facial expressions with a camera and estimate the emotions using a facial expression recognition algorithm. The data collection unit can also record the user's voice and estimate the emotions using voice analysis technology. For example, the data collection unit can analyze the tone and speed of the user's voice to estimate the emotions. The data collection unit determines the priority of the data to be collected based on the estimated emotions. For example, if the user is stressed, data related to stress reduction can be prioritized. Also, if the user is relaxed, data related to relaxation can be prioritized. Also, if the user is excited, data related to excitement can be prioritized. In this way, by prioritizing the data to be collected according to the user's emotions, the burden on the user can be reduced and appropriate data can be collected. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or generative AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the data collection unit may be performed using AI, or may be performed without using AI. For example, the data collection unit may input image data of a user taken with a camera into the generation AI and cause the generation AI to estimate the user's emotions.

[0077] The data collection unit can prioritize collecting highly relevant data by taking into consideration the user's geographical location information when collecting data. For example, the data collection unit prioritizes collecting highly relevant data by taking into consideration the user's geographical location information when collecting data. Geographical location information includes, for example, GPS data and the accuracy of location information. For example, when the user is in a specific area, the data collection unit prioritizes collecting data related to that area. Furthermore, when the user is traveling, the data collection unit can prioritize collecting data related to the travel destination. Furthermore, when the user is at home, the data collection unit can prioritize collecting data related to the home. For example, when the user is in a specific area, the data collection unit prioritizes collecting data related to the area; when the user is traveling, the data collection unit prioritizes collecting data related to the travel destination; and when the user is at home, the data collection unit prioritizes collecting data related to the home. This allows for efficient data collection by collecting highly relevant data by taking into consideration 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 input the user's geographical location information into the generation AI and cause the generation AI to collect highly relevant data.

[0078] The data collection unit can analyze the user's social media activity and collect related data during data collection. For example, the data collection unit can analyze the user's social media activity and collect related data during data collection. Social media activity includes, for example, the content of posts and the number of likes. For example, the data collection unit can collect data related to places where the user has checked in on social media. The data collection unit can also analyze the content of the user's social media posts and collect related data. The data collection unit can also collect related data by referring to the activities of the user's friends on social media. For example, the data collection unit collects data related to places where the user has checked in on social media, analyzes the content of the user's social media posts, collects related data, and collects related data by referring to the activities of the user's friends on social media. In this way, highly relevant data can be collected by analyzing the user's social media activity. Some or all of the above-described processing in the data collection unit can be performed using, for example, AI, or without AI. For example, the data collection unit can input data related to the user's social media activity into the generation AI and cause the generation AI to collect related data.

[0079] 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. The past feedback includes, for example, the type of feedback and the frequency of reflection. The data collection unit adjusts the data collection method, for example, based on feedback provided by the user in the past. The data collection unit can also prioritize the use of a specific data collection means based on the user's past feedback. The data collection unit can also adjust the timing of data collection by referring to the user's past feedback. For example, the data collection unit adjusts the data collection method based on the user's past feedback, prioritizes the use of a specific data collection means based on the user's past feedback, and adjusts the timing of data collection by referring to the user's past feedback. In this way, the collection method can be customized by reflecting the user's past feedback, and data can be collected efficiently. 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 input data regarding the user's past feedback to the generation AI and cause the generation AI to customize the collection method.

[0080] The analysis unit can estimate the user's emotions using a specific method and adjust the analysis presentation method based on the estimated user emotions. The analysis unit, for example, estimates the user's emotions and adjusts the analysis presentation method based on the estimated user emotions. Emotion estimation is performed using specific methods such as facial expression recognition and voice analysis. For example, the analysis unit can capture the user's facial expressions with a camera and estimate the emotions using a facial expression recognition algorithm. The analysis unit can also record the user's voice and estimate the emotions using voice analysis technology. For example, the analysis unit can analyze the tone and speed of the user's voice to estimate the emotions. The analysis unit adjusts the analysis presentation method based on the estimated emotions. For example, if the user is nervous, a simple, highly visible analysis result can be provided. Also, if the user is relaxed, a detailed analysis result can be provided. Also, if the user is in a hurry, a concise analysis result can be provided. By adjusting the analysis presentation method according to the user's emotions, analysis results that are easy for the user to understand can be provided. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the analysis unit may be performed using AI, for example, or may be performed without using AI. For example, the analysis unit may input image data of a user taken with a camera into the generation AI and cause the generation AI to estimate the user's emotions.

[0081] The analysis unit can adjust the level of detail of the analysis based on the importance of the behavior during analysis. For example, the analysis unit adjusts the level of detail of the analysis based on the importance of the behavior during analysis. The importance of the behavior includes, for example, the impact of the behavior and the frequency of the behavior. For example, the analysis unit performs a detailed analysis on a highly important behavior. The analysis unit can also perform a simplified analysis on a less important behavior. The analysis unit can also determine the priority of the analysis based on the importance of the behavior. For example, the analysis unit performs a detailed analysis on a highly important behavior and a simplified analysis on a less important behavior, and determines the priority of the analysis based on the importance of the behavior. In this way, the analysis can be performed efficiently by adjusting the level of detail of the analysis based on the importance of the behavior. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input data regarding the importance of the behavior to the generation AI and cause the generation AI to adjust the level of detail of the analysis.

[0082] The analysis unit can apply different analysis algorithms depending on the category of the behavior during analysis. For example, the analysis unit applies different analysis algorithms depending on the category of the behavior during analysis. Behavior categories include, for example, environmental protection and social contribution. For example, the analysis unit applies an environmental impact assessment algorithm to behavior related to environmental protection. The analysis unit can also apply a health impact assessment algorithm to behavior related to health. The analysis unit can also apply a social impact assessment algorithm to behavior related to social contribution. For example, the analysis unit applies an environmental impact assessment algorithm to behavior related to environmental protection, a health impact assessment algorithm to behavior related to health, and a social impact assessment algorithm to behavior related to social contribution. In this way, by applying different analysis algorithms depending on the category of the behavior, more accurate analysis results can be provided. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input data regarding the category of the behavior into the generation AI and cause the generation AI to apply different analysis algorithms.

[0083] 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, past data and a method for saving the analysis results. For example, the analysis unit can adjust the analysis algorithm based on the user's past analysis results. The analysis unit can also improve the analysis accuracy for specific behaviors based on the user's past analysis results. The analysis unit can also determine the priority of the analysis by referring to the user's past analysis results. For example, the analysis unit can adjust the analysis algorithm based on the user's past analysis results, improve the analysis accuracy for specific behaviors based on the user's past analysis results, and determine the priority of the analysis by referring to the user's past analysis results. This allows the accuracy of the analysis to be improved by referring to the user's past analysis results. Some or all of the above-described processing in the analysis unit can be performed using, for example, AI, or without AI. For example, the analysis unit can input data regarding the user's past analysis results into the generation AI and cause the generation AI to improve the accuracy of the analysis.

[0084] The analysis unit can estimate the user's emotions using a specific method and adjust the length of the analysis based on the estimated user emotions. The analysis unit, for example, estimates the user's emotions and adjusts the length of the analysis based on the estimated user emotions. Emotion estimation is performed using specific methods, such as facial expression recognition or voice analysis. For example, the analysis unit can capture the user's facial expressions with a camera and estimate the emotions using a facial expression recognition algorithm. The analysis unit can also record the user's voice and estimate the emotions using voice analysis technology. For example, the analysis unit can analyze the tone and speed of the user's voice to estimate the emotions. The analysis unit adjusts the length of the analysis based on the estimated emotions. For example, if the user is in a hurry, the analysis unit can provide a short and concise analysis result. Alternatively, if the user is relaxed, the analysis unit can provide a detailed analysis result. Alternatively, if the user is excited, the analysis unit can provide a visually stimulating analysis result. By adjusting the length of the analysis according to the user's emotions, it is possible to provide an analysis result of an appropriate length for the user. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the analysis unit may be performed using AI, for example, or may be performed without using AI. For example, the analysis unit may input image data of a user taken with a camera into the generation AI and cause the generation AI to estimate the user's emotions.

[0085] The analysis unit can determine the priority of analysis based on the time when the behavior was performed during analysis. The analysis unit, for example, determines the priority of analysis based on the time when the behavior was performed during analysis. The time when the behavior was performed includes, for example, the frequency and timing of the behavior. The analysis unit, for example, prioritizes analysis of recently performed behavior. The analysis unit can also postpone analysis of previously performed behavior. The analysis unit can also adjust the level of detail of the analysis depending on the time when the behavior was performed. For example, the analysis unit prioritizes analysis of recently performed behavior and postpones analysis of previously performed behavior, adjusting the level of detail of the analysis depending on the time when the behavior was performed. In this way, determining the priority of analysis based on the time when the behavior was performed enables efficient analysis. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without AI. For example, the analysis unit can input data regarding the time when the behavior was performed to the generation AI and cause the generation AI to determine the analysis priority.

[0086] The analysis unit can adjust the order of analysis based on the relevance of actions during analysis. The analysis unit, for example, adjusts the order of analysis based on the relevance of actions during analysis. The relevance of actions includes, for example, the interrelationship between actions and the influence of actions. The analysis unit, for example, prioritizes analysis of highly relevant actions. The analysis unit can also postpone analysis of less relevant actions. The analysis unit can also adjust the level of detail of the analysis according to the relevance of actions. For example, the analysis unit prioritizes analysis of highly relevant actions and postpones analysis of less relevant actions, adjusting the level of detail of the analysis according to the relevance of actions. In this way, adjusting the order of analysis based on the relevance of actions enables efficient analysis. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without AI. For example, the analysis unit can input data regarding the relevance of actions to the generation AI and cause the generation AI to adjust the order of analysis.

[0087] 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 and educational background. 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 analysis results in simple language. Furthermore, the analysis unit can adjust the level of detail of the analysis results according to the user's level of expertise. For example, if the user has technical expertise, the analysis unit can provide analysis results that use a lot of technical terms, and if the user does not have technical expertise, the analysis unit can provide analysis results in simple language, adjusting the level of detail of the analysis results according to the user's level of expertise. In this way, by adjusting the use of technical terms in the analysis according to the user's level of expertise, analysis results that are easy for the user to understand can be provided. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without AI. For example, the analysis unit can input data regarding the user's level of expertise to the generation AI and cause the generation AI to adjust the use of technical terms in the analysis.

[0088] The score calculation unit can estimate the user's emotion using a specific method and adjust the score calculation method based on the estimated user's emotion. For example, the score calculation unit estimates the user's emotion and adjusts the score calculation method based on the estimated user's emotion. The emotion estimation is performed using a specific method such as facial expression recognition or voice analysis. For example, the score calculation unit captures the user's facial expression with a camera and estimates the emotion using a facial expression recognition algorithm. The score calculation unit can also record the user's voice and estimate the emotion using voice analysis technology. For example, the score calculation unit analyzes the tone and speed of the user's voice to estimate the emotion. The score calculation unit adjusts the score calculation method based on the estimated emotion. For example, if the user is relaxed, a detailed score calculation method can be provided. If the user is in a hurry, a simplified score calculation method can be provided. If the user is excited, a visually stimulating score calculation method can be provided. In this way, by adjusting the score calculation method according to the user's emotion, a score calculation method appropriate for the user can be provided. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-described processing in the score calculation unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the score calculation unit may input image data of a user captured by a camera into the generation AI and cause the generation AI to estimate the user's emotion.

[0089] The score calculation unit can adjust the level of detail of the score based on the importance of the action when calculating the score. For example, the score calculation unit adjusts the level of detail of the score based on the importance of the action when calculating the score. The importance of the action includes, for example, the influence of the action and the frequency of the action. For example, the score calculation unit calculates a detailed score for an action with high importance. The score calculation unit can also calculate a simplified score for an action with low importance. The score calculation unit can also determine the priority of the scores according to the importance of the action. For example, the score calculation unit calculates a detailed score for an action with high importance and a simplified score for an action with low importance, and determines the priority of the scores according to the importance of the action. In this way, the score can be calculated efficiently by adjusting the level of detail of the score based on the importance of the action. Some or all of the above-described processing in the score calculation unit may be performed using, for example, AI, or may be performed without using AI. For example, the score calculation unit can input data regarding the importance of the action to a generation AI and cause the generation AI to adjust the level of detail of the score.

[0090] The score calculation unit can apply different score calculation algorithms depending on the category of the behavior when calculating the score. For example, the score calculation unit applies different score calculation algorithms depending on the category of the behavior when calculating the score. The categories of the behavior include, for example, environmental protection and social contribution. For example, the score calculation unit applies an environmental impact assessment algorithm to behavior related to environmental protection. The score calculation unit can also apply a health impact assessment algorithm to behavior related to health. The score calculation unit can also apply a social impact assessment algorithm to behavior related to social contribution. For example, the score calculation unit applies an environmental impact assessment algorithm to behavior related to environmental protection, a health impact assessment algorithm to behavior related to health, and a social impact assessment algorithm to behavior related to social contribution. In this way, by applying different score calculation algorithms depending on the category of the behavior, more accurate scores can be provided. Some or all of the above-mentioned processing in the score calculation unit may be performed using, for example, AI, or may be performed without using AI. For example, the score calculation unit can input data regarding the category of the behavior to the generation AI and cause the generation AI to apply different score calculation algorithms.

[0091] The score calculation unit can improve the accuracy of the score by referring to the user's past score results when calculating the score. For example, the score calculation unit improves the accuracy of the score by referring to the user's past score results when calculating the score. Past score results include, for example, past data and a method for saving score results. For example, the score calculation unit adjusts the score calculation algorithm based on the user's past score results. The score calculation unit can also improve the score accuracy for a specific behavior based on the user's past score results. The score calculation unit can also determine the priority of the scores by referring to the user's past score results. For example, the score calculation unit adjusts the score calculation algorithm based on the user's past score results to improve the score accuracy for a specific behavior based on the user's past score results, and determines the priority of the scores by referring to the user's past score results. In this way, the accuracy of the score can be improved by referring to the user's past score results. Some or all of the above-described processing in the score calculation unit may be performed using, for example, AI, or may be performed without using AI. For example, the score calculation unit can input data regarding the user's past score results into the generation AI and cause the generation AI to improve the accuracy of the score.

[0092] The score calculation unit can estimate the user's emotion using a specific method and adjust the score display method based on the estimated user emotion. The score calculation unit, for example, estimates the user's emotion and adjusts the score display method based on the estimated user emotion. Emotion estimation is performed using specific methods such as facial expression recognition and voice analysis. For example, the score calculation unit can capture the user's facial expression with a camera and estimate the emotion using a facial expression recognition algorithm. The score calculation unit can also record the user's voice and estimate the emotion using voice analysis technology. For example, the score calculation unit can analyze the tone and speed of the user's voice to estimate the emotion. The score calculation unit adjusts the score display method based on the estimated emotion. For example, if the user is nervous, a simple, highly visible display method can be provided. Also, if the user is relaxed, a display method including detailed information can be provided. Also, if the user is in a hurry, a display method that focuses on the main points can be provided. By adjusting the score display method according to the user's emotion, a score display that is easy for the user to understand can be provided. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the score calculation unit may be performed using, for example, AI, or may be performed without using AI. For example, the score calculation unit may input image data of a user captured by a camera into the generation AI and cause the generation AI to estimate the user's emotions.

[0093] The score calculation unit can determine the priority of scores based on the time when the actions were performed when calculating the scores. For example, the score calculation unit determines the priority of scores based on the time when the actions were performed when calculating the scores. The time when the actions were performed includes, for example, the frequency and timing of the actions. For example, the score calculation unit prioritizes calculating scores for recently performed actions. The score calculation unit can also postpone calculating scores for actions performed in the past. The score calculation unit can also adjust the level of detail of the scores depending on the time when the actions were performed. For example, the score calculation unit prioritizes calculating scores for recently performed actions and postpones calculating scores for actions performed in the past, and adjusts the level of detail of the scores depending on the time when the actions were performed. In this way, by determining the priority of scores based on the time when the actions were performed, scores can be calculated efficiently. Some or all of the above-described processing in the score calculation unit may be performed using, for example, AI, or may be performed without using AI. For example, the score calculation unit can input data regarding the time when the actions were performed to a generation AI and cause the generation AI to determine the priority of the scores.

[0094] The score calculation unit can adjust the order of scores based on the relevance of actions when calculating the scores. For example, the score calculation unit adjusts the order of scores based on the relevance of actions when calculating the scores. The relevance of actions includes, for example, the interrelationship between actions and the influence of actions. For example, the score calculation unit prioritizes calculating scores for highly relevant actions. The score calculation unit can also postpone calculating scores for less relevant actions. The score calculation unit can also adjust the level of detail of the scores according to the relevance of actions. For example, the score calculation unit prioritizes calculating scores for highly relevant actions and postpones calculating scores for less relevant actions, and adjusts the level of detail of the scores according to the relevance of actions. In this way, adjusting the order of scores based on the relevance of actions enables efficient score calculation. Some or all of the above-described processing in the score calculation unit may be performed using, for example, AI, or may be performed without using AI. For example, the score calculation unit can input data regarding the relevance of actions to a generation AI and cause the generation AI to adjust the order of the scores.

[0095] The score calculation unit can adjust the use of technical terms in the score according to the user's level of expertise when calculating the score. For example, the score calculation unit adjusts the use of technical terms in the score according to the user's level of expertise when calculating the score. The level of expertise includes, for example, the user's occupation and educational background. For example, if the user has technical expertise, the score calculation unit provides a score that uses a lot of technical terms. Furthermore, if the user does not have technical expertise, the score calculation unit can provide a score in simple terms. Furthermore, the score calculation unit can adjust the level of detail of the score according to the user's level of expertise. For example, if the user has technical expertise, the score calculation unit provides a score that uses a lot of technical terms, and if the user does not have technical expertise, the score calculation unit provides a score in simple terms, adjusting the level of detail of the score according to the user's level of expertise. In this way, by adjusting the use of technical terms in the score according to the user's level of expertise, it is possible to provide a score that is easy for the user to understand. Some or all of the above-mentioned processing in the score calculation unit may be performed, for example, using AI or without AI. For example, the score calculation unit can input data regarding the user's level of expertise into the generation AI and cause the generation AI to adjust the use of terminology in the score.

[0096] The feedback unit can estimate the user's emotion using a specific method and adjust the feedback expression method based on the estimated user's emotion. For example, the feedback unit estimates the user's emotion and adjusts the feedback expression method based on the estimated user's emotion. Emotion estimation is performed using a specific method, such as facial expression recognition or voice analysis. For example, the feedback unit can capture the user's facial expression with a camera and estimate the emotion using a facial expression recognition algorithm. The feedback unit can also record the user's voice and estimate the emotion using voice analysis technology. For example, the feedback unit can analyze the tone and speed of the user's voice to estimate the emotion. The feedback unit adjusts the feedback expression method based on the estimated emotion. For example, if the user is nervous, the feedback unit can provide simple, highly visible feedback. If the user is relaxed, the feedback unit can provide detailed feedback. If the user is in a hurry, the feedback unit can provide feedback that is easy for the user to understand. By adjusting the feedback expression method according to the user's emotion, it is possible to provide feedback that is easy for the user to understand. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the feedback unit may be performed using AI, or may be performed without using AI. For example, the feedback unit may input image data of a user captured by a camera into the generation AI and cause the generation AI to estimate the user's emotions.

[0097] The feedback unit can adjust the level of detail of the feedback based on the importance of the behavior when providing feedback. For example, the feedback unit adjusts the level of detail of the feedback based on the importance of the behavior when providing feedback. The importance of the behavior includes, for example, the impact of the behavior and the frequency of the behavior. For example, the feedback unit provides detailed feedback for a highly important behavior. The feedback unit can also provide simplified feedback for a less important behavior. The feedback unit can also determine the priority of the feedback based on the importance of the behavior. For example, the feedback unit provides detailed feedback for a highly important behavior and simplified feedback for a less important behavior, and determines the priority of the feedback based on the importance of the behavior. In this way, by adjusting the level of detail of the feedback based on the importance of the behavior, it is possible to provide feedback efficiently. Some or all of the above-described processing in the feedback unit may be performed using, for example, AI, or may be performed without using AI. For example, the feedback unit may input data regarding the importance of the behavior to the generation AI and cause the generation AI to adjust the level of detail of the feedback.

[0098] The feedback unit can apply different feedback algorithms depending on the category of the behavior when providing feedback. For example, the feedback unit applies different feedback algorithms depending on the category of the behavior when providing feedback. The categories of behavior include, for example, environmental protection and social contribution. For example, the feedback unit applies an environmental impact assessment algorithm to behavior related to environmental protection. The feedback unit can also apply a health impact assessment algorithm to behavior related to health. The feedback unit can also apply a social impact assessment algorithm to behavior related to social contribution. For example, the feedback unit applies an environmental impact assessment algorithm to behavior related to environmental protection, a health impact assessment algorithm to behavior related to health, and a social impact assessment algorithm to behavior related to social contribution. In this way, by applying different feedback algorithms depending on the category of the behavior, more accurate feedback can be provided. Some or all of the above-described processing in the feedback unit may be performed using, for example, AI, or may be performed without using AI. For example, the feedback unit can input data regarding the category of the behavior into the generation AI and cause the generation AI to apply different feedback algorithms.

[0099] The feedback unit can improve the accuracy of the feedback by referring to the user's past feedback results when providing feedback. For example, the feedback unit can improve the accuracy of the feedback by referring to the user's past feedback results when providing feedback. Past feedback results include, for example, past data and a method for saving the feedback results. For example, the feedback unit can adjust a feedback algorithm based on the user's past feedback results. The feedback unit can also improve the feedback accuracy for a specific behavior based on the user's past feedback results. The feedback unit can also determine the priority of the feedback by referring to the user's past feedback results. For example, the feedback unit can adjust a feedback algorithm based on the user's past feedback results, improve the feedback accuracy for a specific behavior based on the user's past feedback results, and determine the priority of the feedback by referring to the user's past feedback results. In this way, the accuracy of the feedback can be improved by referring to the user's past feedback results. Some or all of the above-described processing in the feedback unit may be performed using, for example, AI, or may be performed without using AI. For example, the feedback unit can input data regarding the user's past feedback results to the generation AI and cause the generation AI to improve the accuracy of the feedback.

[0100] The feedback unit can estimate the user's emotion using a specific method and adjust the length of the feedback based on the estimated user's emotion. For example, the feedback unit estimates the user's emotion and adjusts the length of the feedback based on the estimated user's emotion. Emotion estimation is performed using a specific method, such as facial expression recognition or voice analysis. For example, the feedback unit can capture the user's facial expression with a camera and estimate the emotion using a facial expression recognition algorithm. The feedback unit can also record the user's voice and estimate the emotion using voice analysis technology. For example, the feedback unit can analyze the tone and speed of the user's voice to estimate the emotion. The feedback unit adjusts the length of the feedback based on the estimated emotion. For example, if the user is in a hurry, the feedback unit can provide short and to-the-point feedback. If the user is relaxed, the feedback unit can provide detailed feedback. If the user is excited, the feedback unit can provide visually stimulating feedback. By adjusting the length of the feedback according to the user's emotion, it is possible to provide feedback of an appropriate length for the user. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the feedback unit may be performed using AI, or may be performed without using AI. For example, the feedback unit may input image data of a user captured by a camera into the generation AI and cause the generation AI to estimate the user's emotions.

[0101] The feedback unit can determine the priority of feedback based on the time when the action was performed when providing feedback. For example, the feedback unit determines the priority of feedback based on the time when the action was performed when providing feedback. The time when the action was performed includes, for example, the frequency of the action and the timing of the action. For example, the feedback unit prioritizes providing feedback for recently performed actions. The feedback unit can also provide feedback later for previously performed actions. The feedback unit can also adjust the level of detail of the feedback depending on the time when the action was performed. For example, the feedback unit prioritizes providing feedback for recently performed actions and provides feedback later for previously performed actions, adjusting the level of detail of the feedback depending on the time when the action was performed. In this way, by determining the priority of feedback based on the time when the action was performed, it is possible to provide feedback efficiently. Some or all of the above-described processing in the feedback unit may be performed using, for example, AI, or may be performed without using AI. For example, the feedback unit can input data regarding the time when the action was performed to the generation AI and cause the generation AI to determine the priority of the feedback.

[0102] The feedback unit can adjust the order of feedback based on the relevance of actions when providing feedback. For example, the feedback unit adjusts the order of feedback based on the relevance of actions when providing feedback. The relevance of actions includes, for example, the interrelationship between actions and the influence of actions. For example, the feedback unit provides feedback preferentially to highly relevant actions. The feedback unit can also provide feedback later to less relevant actions. The feedback unit can also adjust the level of detail of the feedback depending on the relevance of actions. For example, the feedback unit provides feedback preferentially to highly relevant actions and provides feedback later to less relevant actions, adjusting the level of detail of the feedback depending on the relevance of actions. In this way, by adjusting the order of feedback based on the relevance of actions, it is possible to provide feedback efficiently. Some or all of the above-described processing in the feedback unit may be performed using, for example, AI, or may be performed without using AI. For example, the feedback unit can input data regarding the relevance of actions to a generation AI and cause the generation AI to adjust the order of feedback.

[0103] The feedback unit may adjust the use of technical terms in the feedback according to the user's level of expertise when providing feedback. For example, the feedback unit may adjust the use of technical terms in the feedback according to the user's level of expertise when providing feedback. Examples of the level of expertise include the user's occupation and educational background. For example, the feedback unit may provide jargon-heavy feedback when the user has specialized knowledge. Furthermore, the feedback unit may provide feedback in simple language when the user does not have specialized knowledge. Furthermore, the feedback unit may adjust the level of detail of the feedback according to the user's level of expertise. For example, the feedback unit may provide jargon-heavy feedback when the user has specialized knowledge, and provide feedback in simple language when the user does not have specialized knowledge, adjusting the level of detail of the feedback according to the user's level of expertise. This allows for the provision of feedback that is easy for the user to understand by adjusting the use of technical terms in the feedback according to the user's level of expertise. Some or all of the above-described processing in the feedback unit may be performed using, for example, AI, or may be performed without AI. For example, the feedback unit may input data regarding the user's level of expertise to the generation AI and cause the generation AI to adjust the use of technical terms in the feedback. === Hard Collateral 1-1 === Each of the multiple elements, including the data collection unit, analysis unit, score calculation unit, and feedback unit, described above, is implemented, for example, by at least one of the smart device 14 and the data processing device 12. For example, the data collection unit records the user's daily activities using a smartwatch or fitness tracker in the smart device 14 and records the user's route using the smartphone's GPS function. The analysis unit, implemented by the specific processing unit 290 in the data processing device 12, analyzes the collected data using a generative AI and evaluates the contribution of each activity. The score calculation unit, implemented by the specific processing unit 290 in the data processing device 12, calculates an SDG score based on the analysis results. The feedback unit, implemented by the control unit 46A in the smart device 14, provides the calculated score to the user and suggests areas for improvement. === Hard Collateral 1-2 === Each of the multiple elements, including the data collection unit, analysis unit, score calculation unit, and feedback unit, described above, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the data collection unit records the user's daily activities using the camera and sensors of the smart glasses 214 and records the user's travel route using the smartphone's GPS function. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the collected data using a generative AI to evaluate the contribution of each activity. The score calculation unit is realized by the specific processing unit 290 of the data processing device 12 and calculates an SDG score based on the analysis results. The feedback unit is realized by the control unit 46A of the smart glasses 214 and provides the calculated score to the user and suggests areas for improvement. === Hard Collateral 1-3 === Each of the multiple elements, including the data collection unit, analysis unit, score calculation unit, and feedback unit, described above, is realized, for example, by at least one of the headset-type terminal 314 and the data processing device 12. For example, the data collection unit records the user's daily activities using the camera and sensors of the headset-type terminal 314 and records the user's travel route using the smartphone's GPS function. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the collected data using a generative AI to evaluate the contribution of each activity. The score calculation unit is realized by the specific processing unit 290 of the data processing device 12 and calculates an SDG score based on the analysis results. The feedback unit is realized by the control unit 46A of the headset-type terminal 314 and provides the calculated score to the user and indicates areas for improvement. === Hard Collateral 1-4 === Each of the multiple elements, including the data collection unit, analysis unit, score calculation unit, and feedback unit, described above, is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the data collection unit records the user's daily activities using the robot 414's camera and sensors, and records the user's movement route using the smartphone's GPS function. The analysis unit is realized by the specific processing unit 290 of the data processing device 12, analyzes the collected data using a generative AI, and evaluates the contribution of each action. The score calculation unit is realized by the specific processing unit 290 of the data processing device 12, and calculates an SDG score based on the analysis results. The feedback unit is realized by the control unit 46A of the robot 414, and provides the calculated score to the user and indicates areas for improvement.

[0104] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0105] When collecting user behavioral data, the data collection unit can monitor the user's health condition and adjust the frequency and method of data collection based on the user's health condition. For example, if the user is tired, the frequency of data collection can be reduced to reduce the burden on the user. Also, if the user is in good health, the frequency of data collection can be increased to collect more detailed data. Furthermore, if the user is ill or injured, data collection can be temporarily stopped and resumed after recovery. In this way, adjusting the data collection method according to the user's health condition can reduce the burden on the user and collect data at an appropriate time.

[0106] When analyzing the user's behavioral data, the analysis unit can take the user's lifestyle into consideration and adjust the timing and method of analysis based on the lifestyle. For example, if the user is a nocturnal person, the analysis unit can analyze the data at night and provide feedback during the time period when the user is active. Alternatively, if the user is a morning person, the analysis unit can analyze the data in the morning and provide feedback before the user starts their activities. Furthermore, if the user's lifestyle is irregular, the timing of data analysis can be flexibly adjusted to provide feedback tailored to the user's activities. In this way, by adjusting the timing and method of analysis according to the user's lifestyle, feedback can be provided at the optimal time for the user.

[0107] When scoring the user's behavioral data, the score calculation unit can take into account the user's individual goals and values ​​and adjust the score calculation method based on the individual goals and values. For example, if the user values ​​environmental protection, a high score can be assigned to behavior related to environmental protection. Also, if the user values ​​social contribution, a high score can be assigned to behavior related to social contribution. Furthermore, if the user values ​​health, a high score can be assigned to health-related behavior. In this way, by adjusting the score calculation method according to the user's individual goals and values, it is possible to provide a score that is meaningful to the user.

[0108] When providing feedback to a user, the feedback unit can take the user's learning style into consideration and adjust the form and content of the feedback based on the learning style. For example, if the user has a visual learning style, visual feedback using graphs and charts can be provided. If the user has an auditory learning style, feedback using audio messages can be provided. Furthermore, if the user has an experiential learning style, a specific action plan for learning through actual actions can be provided. In this way, by adjusting the form and content of feedback according to the user's learning style, it is possible to provide effective feedback to the user.

[0109] The data collection unit may have a function for protecting the user's privacy when collecting user behavior data. For example, the data collection unit may collect data with the user's consent and anonymize the collected data. The data collection unit may also provide a function that allows the user to set the scope and frequency of data collection. Furthermore, the data collection unit may also provide a function that allows the user to check the collected data and delete it as necessary. This allows the data to be collected while protecting the user's privacy, thereby gaining the user's trust.

[0110] The analysis unit can estimate the user's emotions and adjust the way the analysis results are presented based on the estimated emotions. For example, if the user is feeling stressed, the analysis results can be presented in a simple, easy-to-understand format, reducing the user's burden. If the user is relaxed, detailed analysis results can be presented, allowing the user to gain a deeper understanding. Furthermore, if the user is excited, the analysis results can be presented in a visually stimulating format, attracting the user's interest. In this way, by adjusting the way the analysis results are presented according to the user's emotions, it is possible to provide analysis results that are easy for the user to understand.

[0111] The score calculation unit can estimate the user's emotions and adjust the score display method based on the estimated emotions. For example, if the user is nervous, the score can be displayed in a simple, highly visible format to reduce the user's burden. If the user is relaxed, detailed score information can be provided to allow the user to understand more deeply. Furthermore, if the user is excited, the score can be displayed in a visually stimulating format to attract the user's interest. In this way, by adjusting the score display method according to the user's emotions, a score display that is easy for the user to understand can be provided.

[0112] The feedback unit can estimate the user's emotions and adjust the content of the feedback based on the estimated emotions. For example, if the user is feeling stressed, the feedback unit can provide an encouraging message or advice to relax. If the user is relaxed, the feedback unit can provide specific suggestions for actions or feedback indicating areas for improvement. Furthermore, if the user is excited, the feedback unit can provide feedback suggesting challenging goals or new ideas. In this way, by adjusting the content of the feedback according to the user's emotions, it is possible to provide effective feedback to the user.

[0113] The data collection unit can estimate the user's emotions and adjust the timing of data collection based on the estimated emotions. For example, if the user is feeling stressed, the frequency of data collection can be reduced to reduce the burden on the user. Also, if the user is relaxed, the frequency of data collection can be increased to collect more detailed data. Furthermore, if the user is in a hurry, data collection can be temporarily stopped and resumed later. In this way, by adjusting the timing of data collection according to the user's emotions, the burden on the user can be reduced and data can be collected at appropriate times.

[0114] The analysis unit can estimate the user's emotions and determine the priority of analysis based on the estimated emotions. For example, if the user is feeling stressed, analysis of behaviors related to stress reduction can be prioritized. Also, if the user is relaxed, analysis of behaviors related to relaxation can be prioritized. Furthermore, if the user is excited, analysis of behaviors related to excitement can be prioritized. In this way, by determining the priority of analysis according to the user's emotions, it is possible to provide analysis results that are important to the user.

[0115] The processing flow of the second embodiment will be briefly explained below.

[0116] Step 1: The data collection unit records the user's daily behavior. The user's daily behavior includes commuting method, food choices, energy consumption, etc. The data collection unit automatically collects the user's daily behavior via smartphones and wearable devices. It can also collect data manually entered by the user. Step 2: The analysis unit uses the generation AI to analyze the data collected by the data collection unit and evaluate the degree to which each action contributes to the SDGs. The evaluation is based on specific criteria such as energy consumption and waste reduction. The generation AI uses text generation AI and multimodal generation AI to analyze the collected data and quantify the contribution of each action. Step 3: The score calculation unit calculates an SDG score for each behavior based on the analysis results obtained by the analysis unit. The score is calculated based on specific criteria such as a score range and evaluation items. For example, a score can be assigned on a scale of 0 to 100, a grade of A to F, or percentiles can be calculated. Step 4: The feedback unit provides the user with the score calculated by the score calculation unit and suggests areas for improvement. The feedback is provided in the form of specific suggestions for action or guidelines for improvement. For example, if the score is low, the feedback unit suggests areas for improvement, and if the score is high, the feedback unit provides an encouraging message.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0121] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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).

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0137] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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).

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0153] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0154] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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).

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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).

[0174] 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.

[0175] 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."

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] [Explanation of symbols]

[0189] 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 data collection unit that records the user's daily behavior; an analysis unit that analyzes the data collected by the data collection unit and evaluates the degree to which each action contributes to the SDGs based on specific criteria; a score calculation unit that calculates an SDG score based on the analysis result obtained by the analysis unit; a feedback unit that provides the score calculated by the score calculation unit to the user and indicates areas for improvement. A system characterized by:

2. The data collection unit Automatically collect users' daily behavior through specific smartphones or wearable devices 2. The system of claim 1.

3. The analysis unit Analyze the collected data and evaluate the extent to which each action contributes to the SDGs 2. The system of claim 1.

4. The score calculation unit Calculate an SDG score for each action based on the analysis results 2. The system of claim 1.

5. The feedback unit Provide users with a score and indicate areas for improvement 2. The system of claim 1.

6. The data collection unit Estimate user emotions in a specific way and adjust the timing of data collection based on the estimated user emotions.

2. The system of claim 1.

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

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

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

10. The data collection unit Estimate user emotions in a specific way and prioritize data to be collected based on the estimated user emotions.

2. The system of claim 1.

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