system

Abstract

The system according to the embodiment aims to grasp the CO2 emissions and energy usage status of mobile phones in real time and propose effective reduction actions. [Solution] A system according to an embodiment includes a collection unit, an analysis unit, a proposal unit, and an evaluation unit. The collection unit collects data. The analysis unit analyzes the data collected by the collection unit. The proposal unit makes proposals based on the analysis results obtained by the analysis unit. The evaluation unit evaluates the effectiveness of the actions proposed by the proposal unit.

Description

[Technical Field]

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

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

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

[0004] Conventional technologies do not adequately grasp the CO2 emissions and energy usage status of mobile phones in real time and propose effective reduction actions.

[0005] The system according to the embodiment aims to grasp the CO2 emissions and energy usage status of mobile phones in real time and propose effective reduction actions. [Means for solving the problem]

[0006] A system according to an embodiment includes a collection unit, an analysis unit, a proposal unit, and an evaluation unit. The collection unit collects data. The analysis unit analyzes the data collected by the collection unit. The proposal unit makes proposals based on the analysis results obtained by the analysis unit. The evaluation unit evaluates the effectiveness of the action proposed by the proposal unit. [Effects of the Invention]

[0007] The system according to the embodiment can grasp the CO2 emissions and energy usage status of mobile phones in real time and propose effective reduction actions. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) A platform according to an embodiment of the present invention provides mobile phone users with a means to understand their CO2 emissions and energy usage. The system accesses mobile phone carrier databases and systems and builds APIs and connections to acquire and analyze mobile phone usage data. Next, a generation AI analyzes this data and precisely evaluates users' mobile phone usage patterns and energy consumption in real time. The evaluation results are provided to users, allowing them to understand their own CO2 emissions and energy usage. Furthermore, a function is added to suggest specific actions and effects for energy conservation and CO2 reduction to users interested in carbon neutrality. For example, a system is introduced in which the effectiveness of users' energy-saving and CO2-reduction actions is evaluated and points are awarded based on the reductions. This increases user participation and promotes effective behavior. Through this platform, mobile phone carriers can promote their carbon-neutral initiatives to users, and the generation AI can precisely evaluate and analyze users' mobile phone usage patterns and energy consumption in real time. The suggestion function provides users with specific actions for energy conservation and CO2 reduction, thereby encouraging participation and effective behavior. The commercialization process begins with accessing mobile phone carrier databases and systems and building APIs and connections to acquire and analyze mobile phone usage data. Next, users are encouraged to register and download the app, allowing them to evaluate their CO2 usage, compare it with other users, and use the suggestion function. Marketing and PR activities are also used to promote the service's benefits and effectiveness to users interested in carbon neutrality. In this way, a service that provides information on mobile phone CO2 usage is proposed using generative AI. By allowing mobile phone users to understand their own energy consumption and CO2 emissions, compare them with other users, and suggest specific actions, it is possible to raise interest in carbon neutrality and promote joint efforts between mobile phone carriers and users.This will enable the platform to encourage mobile phone users to understand their own CO2 emissions and energy usage, and to take concrete actions to save energy and reduce CO2 emissions.

[0029] The platform according to the embodiment includes a collection unit, an analysis unit, a proposal unit, and an evaluation unit. The collection unit collects mobile phone usage data. For example, the collection unit accesses a mobile phone carrier's database or system and acquires data through an API or collaboration. The collection unit can collect data such as call history, app usage time, and data traffic. The analysis unit analyzes the collected data and evaluates the user's mobile phone usage patterns and energy consumption status. The analysis unit analyzes the data using, for example, a generation AI and evaluates the user's usage patterns and energy consumption status in real time. For example, the analysis unit can analyze the user's call frequency and app usage time to grasp energy consumption trends. The proposal unit proposes specific actions for energy conservation and CO2 reduction based on the analysis results. For example, the proposal unit proposes actions for energy conservation and CO2 reduction based on the analysis results using, for example, a generation AI. The proposal unit can suggest to the user, for example, using power-saving mode or deleting unnecessary apps. The evaluation unit evaluates the effectiveness of the proposed actions and awards points. The evaluation unit, for example, uses a generation AI to evaluate the effectiveness of the proposed action and award points. For example, the evaluation unit can evaluate the effectiveness of an action performed by a user and award points, thereby increasing the user's motivation to participate. This allows the platform according to the embodiment to encourage mobile phone users to understand their own CO2 emissions and energy usage status and take specific actions to save energy and reduce CO2 emissions. Some or all of the above-described processes in the collection unit, analysis unit, proposal unit, and evaluation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the collection unit may acquire data from a mobile phone carrier's database and input it into the generation AI for analysis. The analysis unit may analyze the data using the generation AI and evaluate the user's usage patterns and energy consumption status. The proposal unit may use the generation AI to propose actions for saving energy and reducing CO2 emissions based on the analysis results. The evaluation unit may use the generation AI to evaluate the effectiveness of the proposed action and award points.This will enable the platform to encourage mobile phone users to understand their own CO2 emissions and energy usage, and to take concrete actions to save energy and reduce CO2 emissions.

[0030] The collection unit can collect mobile phone usage data. Examples of mobile phone usage data include, but are not limited to, call history, app usage time, and data traffic. The collection unit, for example, accesses a mobile phone carrier's database or system and acquires data through an API or collaboration. For example, the collection unit can acquire call history from the mobile phone carrier's database to determine the user's call frequency and call duration. The collection unit can also acquire app usage time to determine which apps the user uses and to what extent. The collection unit can also acquire data traffic to determine the user's data usage. In this way, the collection unit can understand the user's energy consumption status by collecting mobile phone usage data. Some or all of the above-described processing in the collection unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the collection unit can acquire data from a mobile phone carrier's database and input it into a generation AI for analysis.

[0031] The analysis unit can analyze the collected data and evaluate the user's mobile phone usage patterns and energy consumption status. The analysis unit can analyze the data using, for example, a generation AI and evaluate the user's usage patterns and energy consumption status in real time. The analysis unit can, for example, analyze the user's call frequency and app usage time to grasp energy consumption trends. The analysis unit can also analyze data communication volume and evaluate the user's data usage. For example, the analysis unit can evaluate the user's energy consumption trends based on call frequency and call time. The analysis unit can also evaluate which apps the user uses and to what extent based on app usage time. The analysis unit can also evaluate the user's data usage based on data communication volume. In this way, the analysis unit can provide detailed analysis results by evaluating the user's mobile phone usage patterns and energy consumption status. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, a generation AI, or can be performed without using a generation AI. For example, the analysis unit can input the collected data into a generation AI to evaluate the user's usage patterns and energy consumption status.

[0032] The suggestion unit can suggest specific actions for energy saving and CO2 reduction based on the analysis results. The suggestion unit can suggest actions for energy saving and CO2 reduction based on the analysis results, for example, using a generation AI. The suggestion unit can suggest, for example, to the user, using a power-saving mode or deleting unnecessary apps. The suggestion unit can also suggest to the user the use of renewable energy and improving energy efficiency. For example, the suggestion unit can suggest to the user the use of a power-saving mode to reduce energy consumption. The suggestion unit can also suggest to the user the deletion of unnecessary apps to reduce energy consumption. The suggestion unit can also suggest to the user the use of renewable energy to reduce CO2 emissions. In this way, the suggestion unit can encourage user behavior by suggesting specific actions for energy saving and CO2 reduction. Some or all of the above-described processing in the suggestion unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the suggestion unit can input the analysis results into the generation AI to suggest actions for energy saving and CO2 reduction.

[0033] The evaluation unit can evaluate the effect of the proposed action and award points. The evaluation unit, for example, uses a generation AI to evaluate the effect of the proposed action and award points. The evaluation unit, for example, can evaluate the effect of an action performed by a user and award points, thereby increasing the user's motivation to participate. For example, when a user uses a power-saving mode, the evaluation unit can evaluate the effect and award points. Furthermore, when a user deletes an unnecessary app, the evaluation unit can evaluate the effect and award points. Furthermore, when a user uses renewable energy, the evaluation unit can evaluate the effect and award points. In this way, the evaluation unit can evaluate the effect of the proposed action and award points, thereby increasing the user's motivation to participate. Some or all of the above-described processing in the evaluation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the evaluation unit can input the effect of the proposed action into a generation AI and award points.

[0034] The suggestion unit can compare the user with other users. The suggestion unit can, for example, use a generation AI to make the comparison with other users. The suggestion unit can, for example, compare the user's energy consumption status and CO2 emissions with other users and display a ranking. The suggestion unit can also compare the user's energy consumption status and CO2 emissions with average values ​​and display the user's location. For example, the suggestion unit can compare the user's energy consumption status with other users and display a ranking. The suggestion unit can also compare the user's CO2 emissions with other users and display the user's location. The suggestion unit can also compare the user's energy consumption status and CO2 emissions with average values ​​and display the user's location. In this way, the suggestion unit can promote the user's behavior by making a comparison with other users. Some or all of the above-described processing in the suggestion unit can be performed, for example, using the generation AI or without using the generation AI. For example, the suggestion unit can input the comparison with other users to the generation AI and display a ranking.

[0035] The collection unit can analyze the user's past mobile phone usage history and select the optimal data collection method. The collection unit can, for example, use a generation AI to analyze the user's past mobile phone usage history and select the optimal data collection method. The collection unit can, for example, prioritize collecting data from apps that the user frequently used in the past. The collection unit can also select the optimal data collection time period based on the user's past usage patterns. The collection unit can also suggest an efficient data collection method based on the user's past data collection history. For example, the collection unit prioritizes collecting data from apps that the user frequently used in the past. The collection unit can also select the optimal data collection time period based on the user's past usage patterns. The collection unit can also suggest an efficient data collection method based on the user's past data collection history. In this way, the collection unit can select the optimal data collection method by analyzing the user's past usage history. Some or all of the above-mentioned processing in the collection unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the collection unit can input the user's past mobile phone usage history into the generation AI and select the optimal data collection method.

[0036] The collection unit can filter data based on the user's current living situation and areas of interest when collecting data. The collection unit can, for example, use a generation AI to filter data based on the user's current living situation and areas of interest when collecting data. For example, when the user is at work, the collection unit can prioritize collecting work-related data. Furthermore, when the user is on vacation, the collection unit can prioritize collecting travel- and leisure-related data. Furthermore, when the user is interested in health, the collection unit can prioritize collecting health-related data. For example, when the user is at work, the collection unit prioritizes collecting work-related data. Furthermore, when the user is on vacation, the collection unit can prioritize collecting travel- and leisure-related data. Furthermore, when the user is interested in health, the collection unit can prioritize collecting health-related data. In this way, the collection unit can collect highly relevant data by filtering data based on the user's living situation and areas of interest. Some or all of the above-described processing in the collection unit may be performed using the generation AI or may be performed without using the generation AI. For example, when collecting data, the collection unit can input the user's current living situation and areas of interest into the generation AI and perform filtering.

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

[0038] The collection unit can analyze the user's social media activities and collect related data during data collection. The collection unit can, for example, use a generation AI to analyze the user's social media activities and collect related data during data collection. The collection unit can, for example, collect data related to topics in which the user has shown interest on social media. The collection unit can also collect data based on the activity of accounts the user follows. Furthermore, the collection unit can collect data related to content posted by the user. For example, the collection unit collects data related to topics in which the user has shown interest on social media. The collection unit can also collect data based on the activity of accounts the user follows. Furthermore, the collection unit can collect data related to content posted by the user. In this way, the collection unit can collect related data by analyzing the user's social media activities. Some or all of the above-mentioned processing in the collection unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the collection unit can input the user's social media activities into the generation AI during data collection and collect related data.

[0039] The analysis unit can adjust the level of detail of the analysis based on the importance of the data during analysis. The analysis unit can adjust the level of detail of the analysis based on the importance of the data during analysis, for example, using a generation AI. The analysis unit can, for example, perform a detailed analysis on data of high importance. The analysis unit can also perform a simplified analysis on data of low importance. Furthermore, the analysis unit can perform an analysis with an appropriate level of detail on data of medium importance. For example, the analysis unit can perform a detailed analysis on data of high importance. The analysis unit can also perform a simplified analysis on data of low importance. Furthermore, the analysis unit can perform an analysis with an appropriate level of detail on data of medium importance. In this way, the analysis unit can perform an efficient analysis by adjusting the level of detail of the analysis based on the importance of the data. Some or all of the above-mentioned processing in the analysis unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the analysis unit can input the importance of the data to the generation AI and adjust the level of detail of the analysis.

[0040] The analysis unit can apply different analysis algorithms depending on the data category during analysis. For example, the analysis unit can apply different analysis algorithms depending on the data category during analysis using the generation AI. For example, the analysis unit can apply an energy efficiency analysis algorithm to energy consumption data. The analysis unit can also apply an emissions analysis algorithm to CO2 emission data. Furthermore, the analysis unit can apply a behavior analysis algorithm to usage pattern data. For example, the analysis unit can apply an energy efficiency analysis algorithm to energy consumption data. The analysis unit can also apply an emissions analysis algorithm to CO2 emission data. Furthermore, the analysis unit can apply a behavior analysis algorithm to usage pattern data. In this way, the analysis unit can provide appropriate analysis results by applying different analysis algorithms depending on the data category. Some or all of the above-mentioned processing in the analysis unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the analysis unit can input the data category to the generation AI and apply different analysis algorithms.

[0041] The analysis unit can determine the analysis priority based on the time when the data was collected during analysis. The analysis unit can determine the analysis priority based on the time when the data was collected during analysis, for example, using the generation AI. The analysis unit can, for example, prioritize analyzing the most recent data. The analysis unit can also analyze the data by emphasizing current data while referring to past data. The analysis unit can also analyze the data from a specific period by priority. For example, the analysis unit can analyze the most recent data by priority. The analysis unit can analyze the data by emphasizing current data while referring to past data. The analysis unit can also analyze the data from a specific period by priority. In this way, the analysis unit can prioritize analyzing the most recent data by determining the analysis priority based on the time when the data was collected. Some or all of the above-described processing in the analysis unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the analysis unit can input the time when the data was collected into the generation AI to determine the analysis priority.

[0042] The analysis unit can adjust the order of analysis based on the relevance of the data during analysis. The analysis unit can adjust the order of analysis based on the relevance of the data during analysis, for example, using a generation AI. The analysis unit can, for example, prioritize analysis of highly relevant data. The analysis unit can also postpone analysis of less relevant data. Furthermore, the analysis unit can dynamically adjust the order of analysis according to the relevance of the data. For example, the analysis unit prioritizes analysis of highly relevant data. The analysis unit can also postpone analysis of less relevant data. Furthermore, the analysis unit can dynamically adjust the order of analysis according to the relevance of the data. In this way, the analysis unit can perform efficient analysis by adjusting the order of analysis based on the relevance of the data. Some or all of the above-mentioned processing in the analysis unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the analysis unit can input the relevance of the data into the generation AI and adjust the order of analysis.

[0043] The suggestion unit can adjust the level of detail of the proposal based on the importance of the action when making the suggestion. The suggestion unit can adjust the level of detail of the proposal based on the importance of the action when making the suggestion, for example, using a generation AI. The suggestion unit can, for example, make a detailed suggestion for an action with high importance. The suggestion unit can also make a simplified suggestion for an action with low importance. Furthermore, the suggestion unit can make a moderately detailed suggestion for an action with medium importance. For example, the suggestion unit can make a detailed suggestion for an action with high importance. The suggestion unit can also make a simplified suggestion for an action with low importance. Furthermore, the suggestion unit can make a moderately detailed suggestion for an action with medium importance. In this way, the suggestion unit can make an efficient suggestion by adjusting the level of detail of the suggestion based on the importance of the action. Some or all of the above-mentioned processing in the suggestion unit may be performed using the generation AI or may be performed without using the generation AI. For example, the suggestion unit can input the importance of the action to the generation AI and adjust the level of detail of the suggestion.

[0044] The suggestion unit can apply different suggestion algorithms depending on the category of the action when making a suggestion. For example, the suggestion unit can use a generation AI to apply different suggestion algorithms depending on the category of the action when making a suggestion. For example, the suggestion unit can apply an energy efficiency suggestion algorithm to an energy-saving action. The suggestion unit can also apply an emission reduction suggestion algorithm to a CO2 reduction action. Furthermore, the suggestion unit can apply a behavior improvement suggestion algorithm to a usage pattern improvement action. For example, the suggestion unit can apply an energy efficiency suggestion algorithm to an energy-saving action. The suggestion unit can also apply an emission reduction suggestion algorithm to a CO2 reduction action. Furthermore, the suggestion unit can apply a behavior improvement suggestion algorithm to a usage pattern improvement action. In this way, the suggestion unit can provide appropriate suggestion results by applying different suggestion algorithms depending on the category of the action. Some or all of the above-mentioned processing in the suggestion unit may be performed using or without the generation AI. For example, the suggestion unit can input the category of the action to the generation AI and apply different suggestion algorithms.

[0045] The suggestion unit can determine the priority of the suggestions based on the execution time of the actions when making the suggestions. The suggestion unit can determine the priority of the suggestions based on the execution time of the actions when making the suggestions, for example, using a generation AI. The suggestion unit can, for example, prioritize suggestions of actions that can be executed immediately. The suggestion unit can also postpone suggestions of actions that will be executed in the long term. Furthermore, the suggestion unit can dynamically adjust the priority of the suggestions according to the execution time of the actions. For example, the suggestion unit prioritizes suggestions of actions that can be executed immediately. The suggestion unit can also postpone suggestions of actions that will be executed in the long term. Furthermore, the suggestion unit can dynamically adjust the priority of the suggestions according to the execution time of the actions. In this way, the suggestion unit can make efficient suggestions by determining the priority of the suggestions based on the execution time of the actions. Some or all of the above-described processing in the suggestion unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the suggestion unit can input the execution time of the actions to the generation AI and determine the priority of the suggestions.

[0046] The suggestion unit can adjust the order of suggestions based on the relevance of actions when making suggestions. The suggestion unit can adjust the order of suggestions based on the relevance of actions when making suggestions, for example, using a generation AI. The suggestion unit can, for example, preferentially suggest highly relevant actions. The suggestion unit can also postpone suggesting less relevant actions. Furthermore, the suggestion unit can dynamically adjust the order of suggestions according to the relevance of actions. For example, the suggestion unit preferentially suggests highly relevant actions. The suggestion unit can also postpone suggesting less relevant actions. Furthermore, the suggestion unit can dynamically adjust the order of suggestions according to the relevance of actions. As a result, the suggestion unit can make efficient suggestions by adjusting the order of suggestions based on the relevance of actions. Some or all of the above-described processing in the suggestion unit may be performed using or without the generation AI. For example, the suggestion unit can input the relevance of actions to the generation AI and adjust the order of suggestions.

[0047] The evaluation unit can analyze the user's past action history and select the optimal evaluation method during evaluation. For example, the evaluation unit uses a generation AI to analyze the user's past action history and select the optimal evaluation method during evaluation. The evaluation unit can perform evaluation based on the effects of actions previously performed by the user. The evaluation unit can also select optimal evaluation criteria from the user's past action history. Furthermore, the evaluation unit can evaluate the current action while referring to the user's past action history. For example, the evaluation unit performs evaluation based on the effects of actions previously performed by the user. The evaluation unit can select optimal evaluation criteria from the user's past action history. Furthermore, the evaluation unit can evaluate the current action while referring to the user's past action history. In this way, the evaluation unit can select the optimal evaluation method by analyzing the user's past action history. Some or all of the above-mentioned processing in the evaluation unit may be performed using the generation AI or may be performed without using the generation AI. For example, the evaluation unit can input the user's past action history into the generation AI and select the optimal evaluation method.

[0048] The evaluation unit can customize the evaluation means based on the user's current living situation during evaluation. The evaluation unit, for example, uses a generation AI to customize the evaluation means based on the user's current living situation during evaluation. For example, if the user is at work, the evaluation unit can provide a work-related evaluation method. Also, if the user is on vacation, the evaluation unit can provide a vacation-related evaluation method. Furthermore, if the user is interested in health, the evaluation unit can provide a health-related evaluation method. For example, if the user is at work, the evaluation unit can provide a work-related evaluation method. Also, if the user is on vacation, the evaluation unit can provide a vacation-related evaluation method. Furthermore, if the user is interested in health, the evaluation unit can provide a health-related evaluation method. In this way, the evaluation unit can provide an appropriate evaluation result by customizing the evaluation means based on the user's living situation. Some or all of the above-mentioned processing in the evaluation unit may be performed using or without the generation AI. For example, the evaluation unit can input the user's current living situation into the generation AI to customize the evaluation means.

[0049] The evaluation unit can select the optimal evaluation method by taking into account the user's geographical location information during evaluation. For example, the evaluation unit uses a generation AI to select the optimal evaluation method by taking into account the user's geographical location information during evaluation. For example, if the user is in a specific area, the evaluation unit can provide a evaluation method related to that area. Also, if the user is traveling, the evaluation unit can provide a evaluation method related to the travel destination. Furthermore, if the user is at home, the evaluation unit can provide a evaluation method for the area around the user's home. For example, if the user is in a specific area, the evaluation unit can provide a evaluation method related to that area. Also, if the user is traveling, the evaluation unit can provide a evaluation method related to the travel destination. Furthermore, if the user is at home, the evaluation unit can provide a evaluation method for the area around the user's home. This allows the evaluation unit to select the optimal evaluation method by taking into account the user's geographical location information. Some or all of the above-mentioned processing in the evaluation unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the evaluation unit can input the user's geographical location information into the generation AI and select the optimal evaluation method.

[0050] The evaluation unit can analyze the user's social media activity and suggest evaluation measures during evaluation. The evaluation unit can, for example, use a generation AI to analyze the user's social media activity and suggest evaluation measures during evaluation. The evaluation unit can, for example, provide evaluation methods related to topics in which the user has shown interest on social media. The evaluation unit can also suggest evaluation methods based on the activity of accounts the user follows. Furthermore, the evaluation unit can provide evaluation methods related to content posted by the user. For example, the evaluation unit can provide evaluation methods related to topics in which the user has shown interest on social media. The evaluation unit can also suggest evaluation methods based on the activity of accounts the user follows. Furthermore, the evaluation unit can provide evaluation methods related to content posted by the user. In this way, the evaluation unit can suggest related evaluation measures by analyzing the user's social media activity. Some or all of the above-mentioned processing in the evaluation unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the evaluation unit can input the user's social media activity into the generation AI and suggest evaluation measures.

[0051] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0052] The collection unit can analyze the user's past data collection history and select the optimal data collection method. For example, it can prioritize collection of data from apps that the user frequently used in the past. It can also select the optimal data collection time period based on the user's past usage patterns. Furthermore, it can suggest an efficient data collection method based on the user's past data collection history. In this way, the collection unit can select the optimal data collection method by analyzing the user's past usage history. Some or all of the above-mentioned processing in the collection unit may be performed using or without the generation AI. For example, the collection unit can input the user's past mobile phone usage history into the generation AI and select the optimal data collection method.

[0053] The analysis unit can adjust the level of detail of the analysis based on the importance of the data. For example, a detailed analysis can be performed on data with high importance. A simplified analysis can be performed on data with low importance. Furthermore, an analysis with an appropriate level of detail can be performed on data with medium importance. In this way, the analysis unit can perform an efficient analysis by adjusting the level of detail of the analysis based on the importance of the data. Some or all of the above-described processing in the analysis unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the analysis unit can input the importance of the data into the generation AI and adjust the level of detail of the analysis.

[0054] The suggestion unit can adjust the level of detail of the suggestion based on the importance of the action when making a suggestion. For example, a detailed suggestion can be made for an action with high importance. A simplified suggestion can be made for an action with low importance. Furthermore, a suggestion with an appropriate level of detail can be made for an action with medium importance. In this way, the suggestion unit can make an efficient suggestion by adjusting the level of detail of the suggestion based on the importance of the action. Some or all of the above-described processing in the suggestion unit may be performed using or without using the generation AI. For example, the suggestion unit can input the importance of the action to the generation AI and adjust the level of detail of the suggestion.

[0055] During evaluation, the evaluation unit can analyze the user's past action history to select the optimal evaluation method. For example, the evaluation can be performed based on the effects of actions performed by the user in the past. The evaluation unit can also select optimal evaluation criteria from the user's past action history. Furthermore, the current action can be evaluated while referring to the user's past action history. This allows the evaluation unit to select the optimal evaluation method by analyzing the user's past action history. Some or all of the above-mentioned processing in the evaluation unit may be performed using or without the generation AI. For example, the evaluation unit can input the user's past action history into the generation AI to select the optimal evaluation method.

[0056] When collecting data, the collection unit can prioritize collecting highly relevant data by taking into account the user's geographical location information. For example, when the user is in a specific area, data related to that area can be prioritized. Also, when the user is traveling, data related to the travel destination can be prioritized. Furthermore, when the user is at home, data around the home can be prioritized. In this way, the collection unit can prioritize collecting highly relevant data by taking into account the user's geographical location information. Some or all of the above-mentioned processing in the collection unit may be performed using or without the generation AI. For example, when collecting data, the collection unit can input the user's geographical location information into the generation AI and prioritize collecting highly relevant data.

[0057] The processing flow of the first embodiment will be briefly explained below.

[0058] Step 1: The collection unit collects mobile phone usage data. For example, the collection unit accesses the mobile phone carrier's database or system and obtains data through APIs or collaboration. The collection unit can collect data such as call history, app usage time, and data traffic. Step 2: The analysis unit analyzes the collected data and evaluates the user's mobile phone usage patterns and energy consumption status. The analysis unit analyzes the data using, for example, generative AI and evaluates the user's usage patterns and energy consumption status in real time. The analysis unit can, for example, analyze the user's call frequency and app usage time to understand energy consumption trends. Step 3: The suggestion unit proposes specific actions to save energy and reduce CO2 emissions based on the analysis results. For example, the suggestion unit uses a generative AI to propose actions to save energy and reduce CO2 emissions based on the analysis results. For example, the suggestion unit can suggest to the user that they use a power-saving mode or delete unnecessary apps. Step 4: The evaluation unit evaluates the effectiveness of the proposed action and awards points. The evaluation unit, for example, uses a generation AI to evaluate the effectiveness of the proposed action and award points. For example, the evaluation unit evaluates the effectiveness of the action performed by the user and awards points, thereby increasing the user's motivation to participate.

[0059] (Example 2) A platform according to an embodiment of the present invention provides mobile phone users with a means to understand their CO2 emissions and energy usage. The system accesses mobile phone carrier databases and systems and builds APIs and connections to acquire and analyze mobile phone usage data. Next, a generation AI analyzes this data and precisely evaluates users' mobile phone usage patterns and energy consumption in real time. The evaluation results are provided to users, allowing them to understand their own CO2 emissions and energy usage. Furthermore, a function is added to suggest specific actions and effects for energy conservation and CO2 reduction to users interested in carbon neutrality. For example, a system is introduced in which the effectiveness of users' energy-saving and CO2-reduction actions is evaluated and points are awarded based on the reductions. This increases user participation and promotes effective behavior. Through this platform, mobile phone carriers can promote their carbon-neutral initiatives to users, and the generation AI can precisely evaluate and analyze users' mobile phone usage patterns and energy consumption in real time. The suggestion function provides users with specific actions for energy conservation and CO2 reduction, thereby encouraging participation and effective behavior. The commercialization process begins with accessing mobile phone carrier databases and systems and building APIs and connections to acquire and analyze mobile phone usage data. Next, users are encouraged to register and download the app, allowing them to evaluate their CO2 usage, compare it with other users, and use the suggestion function. Marketing and PR activities are also used to promote the service's benefits and effectiveness to users interested in carbon neutrality. In this way, a service that provides information on mobile phone CO2 usage is proposed using generative AI. By allowing mobile phone users to understand their own energy consumption and CO2 emissions, compare them with other users, and suggest specific actions, it is possible to raise interest in carbon neutrality and promote joint efforts between mobile phone carriers and users.This will enable the platform to encourage mobile phone users to understand their own CO2 emissions and energy usage, and to take concrete actions to save energy and reduce CO2 emissions.

[0060] The platform according to the embodiment includes a collection unit, an analysis unit, a proposal unit, and an evaluation unit. The collection unit collects mobile phone usage data. For example, the collection unit accesses a mobile phone carrier's database or system and acquires data through an API or collaboration. The collection unit can collect data such as call history, app usage time, and data traffic. The analysis unit analyzes the collected data and evaluates the user's mobile phone usage patterns and energy consumption status. The analysis unit analyzes the data using, for example, a generation AI and evaluates the user's usage patterns and energy consumption status in real time. For example, the analysis unit can analyze the user's call frequency and app usage time to grasp energy consumption trends. The proposal unit proposes specific actions for energy conservation and CO2 reduction based on the analysis results. For example, the proposal unit proposes actions for energy conservation and CO2 reduction based on the analysis results using, for example, a generation AI. The proposal unit can suggest to the user, for example, using power-saving mode or deleting unnecessary apps. The evaluation unit evaluates the effectiveness of the proposed actions and awards points. The evaluation unit, for example, uses a generation AI to evaluate the effectiveness of the proposed action and award points. For example, the evaluation unit can evaluate the effectiveness of an action performed by a user and award points, thereby increasing the user's motivation to participate. This allows the platform according to the embodiment to encourage mobile phone users to understand their own CO2 emissions and energy usage status and take specific actions to save energy and reduce CO2 emissions. Some or all of the above-described processes in the collection unit, analysis unit, proposal unit, and evaluation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the collection unit may acquire data from a mobile phone carrier's database and input it into the generation AI for analysis. The analysis unit may analyze the data using the generation AI and evaluate the user's usage patterns and energy consumption status. The proposal unit may use the generation AI to propose actions for saving energy and reducing CO2 emissions based on the analysis results. The evaluation unit may use the generation AI to evaluate the effectiveness of the proposed action and award points.This will enable the platform to encourage mobile phone users to understand their own CO2 emissions and energy usage, and to take concrete actions to save energy and reduce CO2 emissions.

[0061] The collection unit can collect mobile phone usage data. Examples of mobile phone usage data include, but are not limited to, call history, app usage time, and data traffic. The collection unit, for example, accesses a mobile phone carrier's database or system and acquires data through an API or collaboration. For example, the collection unit can acquire call history from the mobile phone carrier's database to determine the user's call frequency and call duration. The collection unit can also acquire app usage time to determine which apps the user uses and to what extent. The collection unit can also acquire data traffic to determine the user's data usage. In this way, the collection unit can understand the user's energy consumption status by collecting mobile phone usage data. Some or all of the above-described processing in the collection unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the collection unit can acquire data from a mobile phone carrier's database and input it into a generation AI for analysis.

[0062] The analysis unit can analyze the collected data and evaluate the user's mobile phone usage patterns and energy consumption status. The analysis unit can analyze the data using, for example, a generation AI and evaluate the user's usage patterns and energy consumption status in real time. The analysis unit can, for example, analyze the user's call frequency and app usage time to grasp energy consumption trends. The analysis unit can also analyze data communication volume and evaluate the user's data usage. For example, the analysis unit can evaluate the user's energy consumption trends based on call frequency and call time. The analysis unit can also evaluate which apps the user uses and to what extent based on app usage time. The analysis unit can also evaluate the user's data usage based on data communication volume. In this way, the analysis unit can provide detailed analysis results by evaluating the user's mobile phone usage patterns and energy consumption status. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, a generation AI, or can be performed without using a generation AI. For example, the analysis unit can input the collected data into a generation AI to evaluate the user's usage patterns and energy consumption status.

[0063] The suggestion unit can suggest specific actions for energy saving and CO2 reduction based on the analysis results. The suggestion unit can suggest actions for energy saving and CO2 reduction based on the analysis results, for example, using a generation AI. The suggestion unit can suggest, for example, to the user, using a power-saving mode or deleting unnecessary apps. The suggestion unit can also suggest to the user the use of renewable energy and improving energy efficiency. For example, the suggestion unit can suggest to the user the use of a power-saving mode to reduce energy consumption. The suggestion unit can also suggest to the user the deletion of unnecessary apps to reduce energy consumption. The suggestion unit can also suggest to the user the use of renewable energy to reduce CO2 emissions. In this way, the suggestion unit can encourage user behavior by suggesting specific actions for energy saving and CO2 reduction. Some or all of the above-described processing in the suggestion unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the suggestion unit can input the analysis results into the generation AI to suggest actions for energy saving and CO2 reduction.

[0064] The evaluation unit can evaluate the effect of the proposed action and award points. The evaluation unit, for example, uses a generation AI to evaluate the effect of the proposed action and award points. The evaluation unit, for example, can evaluate the effect of an action performed by a user and award points, thereby increasing the user's motivation to participate. For example, when a user uses a power-saving mode, the evaluation unit can evaluate the effect and award points. Furthermore, when a user deletes an unnecessary app, the evaluation unit can evaluate the effect and award points. Furthermore, when a user uses renewable energy, the evaluation unit can evaluate the effect and award points. In this way, the evaluation unit can evaluate the effect of the proposed action and award points, thereby increasing the user's motivation to participate. Some or all of the above-described processing in the evaluation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the evaluation unit can input the effect of the proposed action into a generation AI and award points.

[0065] The suggestion unit can compare the user with other users. The suggestion unit can, for example, use a generation AI to make the comparison with other users. The suggestion unit can, for example, compare the user's energy consumption status and CO2 emissions with other users and display a ranking. The suggestion unit can also compare the user's energy consumption status and CO2 emissions with average values ​​and display the user's location. For example, the suggestion unit can compare the user's energy consumption status with other users and display a ranking. The suggestion unit can also compare the user's CO2 emissions with other users and display the user's location. The suggestion unit can also compare the user's energy consumption status and CO2 emissions with average values ​​and display the user's location. In this way, the suggestion unit can promote the user's behavior by making a comparison with other users. Some or all of the above-described processing in the suggestion unit can be performed, for example, using the generation AI or without using the generation AI. For example, the suggestion unit can input the comparison with other users to the generation AI and display a ranking.

[0066] The collection unit can estimate the user's emotions and adjust the timing of data collection based on the estimated user emotions. For example, the collection unit can estimate the user's emotions using generative AI and adjust the timing of data collection based on the estimated emotions. For example, if the user is feeling stressed, the collection unit can reduce the frequency of data collection to reduce the burden. Also, if the user is relaxed, the collection unit can increase the frequency of data collection to collect more detailed data. Furthermore, if the user is in a hurry, the collection unit can temporarily stop data collection and resume it later. For example, the collection unit can capture the user's facial expressions with a camera and estimate the emotions using an emotion estimation algorithm. For example, the collection unit can calculate an emotion score based on changes in facial expressions and adjust the timing of data collection. Also, the collection unit can record the user's voice and estimate the emotions using voice analysis technology. For example, the collection unit can analyze the tone and speed of the voice to calculate an emotion score and adjust the timing of data collection. Furthermore, the collection unit can collect the user's biometric data (heart rate and electrodermal activity) using a sensor and estimate emotions using an emotion estimation algorithm. The collection unit, for example, calculates an emotion score based on heart rate fluctuations and adjusts the timing of data collection. This allows the collection unit to adjust the timing of data collection based on the user's emotions, thereby reducing the burden on the user. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the collection unit may be performed using or without the generation AI. For example, the collection unit can input the user's emotion data into the generation AI and adjust the timing of data collection.

[0067] The collection unit can analyze the user's past mobile phone usage history and select the optimal data collection method. The collection unit can, for example, use a generation AI to analyze the user's past mobile phone usage history and select the optimal data collection method. The collection unit can, for example, prioritize collecting data from apps that the user frequently used in the past. The collection unit can also select the optimal data collection time period based on the user's past usage patterns. The collection unit can also suggest an efficient data collection method based on the user's past data collection history. For example, the collection unit prioritizes collecting data from apps that the user frequently used in the past. The collection unit can also select the optimal data collection time period based on the user's past usage patterns. The collection unit can also suggest an efficient data collection method based on the user's past data collection history. In this way, the collection unit can select the optimal data collection method by analyzing the user's past usage history. Some or all of the above-mentioned processing in the collection unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the collection unit can input the user's past mobile phone usage history into the generation AI and select the optimal data collection method.

[0068] The collection unit can filter data based on the user's current living situation and areas of interest when collecting data. The collection unit can, for example, use a generation AI to filter data based on the user's current living situation and areas of interest when collecting data. For example, when the user is at work, the collection unit can prioritize collecting work-related data. Furthermore, when the user is on vacation, the collection unit can prioritize collecting travel- and leisure-related data. Furthermore, when the user is interested in health, the collection unit can prioritize collecting health-related data. For example, when the user is at work, the collection unit prioritizes collecting work-related data. Furthermore, when the user is on vacation, the collection unit can prioritize collecting travel- and leisure-related data. Furthermore, when the user is interested in health, the collection unit can prioritize collecting health-related data. In this way, the collection unit can collect highly relevant data by filtering data based on the user's living situation and areas of interest. Some or all of the above-described processing in the collection unit may be performed using the generation AI or may be performed without using the generation AI. For example, when collecting data, the collection unit can input the user's current living situation and areas of interest into the generation AI and perform filtering.

[0069] The collection unit can estimate the user's emotions and determine the priority of data to be collected based on the estimated user emotions. The collection unit can estimate the user's emotions using, for example, a generation AI and determine the priority of data to be collected based on the estimated emotions. For example, if the user is feeling stressed, the collection unit can prioritize collecting data that helps reduce stress. Furthermore, if the user is relaxed, the collection unit can prioritize collecting data to help maintain relaxation. Furthermore, if the user is in a hurry, the collection unit can prioritize collecting data that helps save time. For example, if the user is feeling stressed, the collection unit prioritizes collecting data that helps reduce stress. Furthermore, if the user is relaxed, the collection unit can prioritize collecting data to help maintain relaxation. Furthermore, if the user is in a hurry, the collection unit can prioritize collecting data that helps save time. In this way, the collection unit can prioritize collecting data that is important to the user by determining the priority of data based on the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the collection unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the collection unit may input user emotion data into the generation AI and determine the priority of the data to be collected.

[0070] When collecting data, the collection unit can prioritize collecting highly relevant data by taking into account the user's geographical location information. When collecting data, the collection unit, for example, uses a generation AI to prioritize collecting highly relevant data by taking into account the user's geographical location information. For example, when the user is in a specific area, the collection unit can prioritize collecting data related to that area. Furthermore, when the user is traveling, the collection unit can prioritize collecting data related to the travel destination. Furthermore, when the user is at home, the collection unit can prioritize collecting data around the user's home. For example, when the user is in a specific area, the collection unit prioritizes collecting data related to that area. Furthermore, when the user is traveling, the collection unit can prioritize collecting data related to the travel destination. Furthermore, when the user is at home, the collection unit can prioritize collecting data around the user's home. In this way, the collection unit can prioritize collecting highly relevant data by taking into account the user's geographical location information. Some or all of the above-mentioned processing in the collection unit may be performed using the generation AI, or may be performed without using the generation AI. For example, when collecting data, the collection unit can input the user's geographical location information into the generation AI and prioritize the collection of highly relevant data.

[0071] The collection unit can analyze the user's social media activities and collect related data during data collection. The collection unit can, for example, use a generation AI to analyze the user's social media activities and collect related data during data collection. The collection unit can, for example, collect data related to topics in which the user has shown interest on social media. The collection unit can also collect data based on the activity of accounts the user follows. Furthermore, the collection unit can collect data related to content posted by the user. For example, the collection unit collects data related to topics in which the user has shown interest on social media. The collection unit can also collect data based on the activity of accounts the user follows. Furthermore, the collection unit can collect data related to content posted by the user. In this way, the collection unit can collect related data by analyzing the user's social media activities. Some or all of the above-mentioned processing in the collection unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the collection unit can input the user's social media activities into the generation AI during data collection and collect related data.

[0072] The analysis unit can estimate the user's emotion and adjust the presentation method of the analysis based on the estimated emotion. The analysis unit can estimate the user's emotion using, for example, a generation AI and adjust the presentation method of the analysis based on the estimated emotion. For example, if the user is stressed, the analysis unit can provide a simple and highly visible analysis result. Furthermore, if the user is relaxed, the analysis unit can provide a detailed analysis result. Furthermore, if the user is in a hurry, the analysis unit can provide a summary of the main points. For example, if the user is stressed, the analysis unit can provide a simple and highly visible analysis result. Furthermore, if the user is relaxed, the analysis unit can provide a detailed analysis result. Furthermore, if the user is in a hurry, the analysis unit can provide a summary of the main points. In this way, the analysis unit can adjust the presentation method of the analysis based on the user's emotion, thereby providing an analysis result that is easy for the user to understand. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the analysis unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the analysis unit may input user emotion data into the generation AI and adjust the way the analysis is expressed.

[0073] The analysis unit can adjust the level of detail of the analysis based on the importance of the data during analysis. The analysis unit can adjust the level of detail of the analysis based on the importance of the data during analysis, for example, using a generation AI. The analysis unit can, for example, perform a detailed analysis on data of high importance. The analysis unit can also perform a simplified analysis on data of low importance. Furthermore, the analysis unit can perform an analysis with an appropriate level of detail on data of medium importance. For example, the analysis unit can perform a detailed analysis on data of high importance. The analysis unit can also perform a simplified analysis on data of low importance. Furthermore, the analysis unit can perform an analysis with an appropriate level of detail on data of medium importance. In this way, the analysis unit can perform an efficient analysis by adjusting the level of detail of the analysis based on the importance of the data. Some or all of the above-mentioned processing in the analysis unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the analysis unit can input the importance of the data to the generation AI and adjust the level of detail of the analysis.

[0074] The analysis unit can apply different analysis algorithms depending on the data category during analysis. For example, the analysis unit can apply different analysis algorithms depending on the data category during analysis using the generation AI. For example, the analysis unit can apply an energy efficiency analysis algorithm to energy consumption data. The analysis unit can also apply an emissions analysis algorithm to CO2 emission data. Furthermore, the analysis unit can apply a behavior analysis algorithm to usage pattern data. For example, the analysis unit can apply an energy efficiency analysis algorithm to energy consumption data. The analysis unit can also apply an emissions analysis algorithm to CO2 emission data. Furthermore, the analysis unit can apply a behavior analysis algorithm to usage pattern data. In this way, the analysis unit can provide appropriate analysis results by applying different analysis algorithms depending on the data category. Some or all of the above-mentioned processing in the analysis unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the analysis unit can input the data category to the generation AI and apply different analysis algorithms.

[0075] The analysis unit can estimate the user's emotion and adjust the length of the analysis based on the estimated user's emotion. The analysis unit can estimate the user's emotion using, for example, a generation AI and adjust the length of the analysis based on the estimated emotion. For example, if the user is stressed, the analysis unit can provide a short and concise analysis result. Also, if the user is relaxed, the analysis unit can provide a detailed analysis result. Furthermore, if the user is in a hurry, the analysis unit can provide a concise analysis result. For example, if the user is stressed, the analysis unit can provide a short and concise analysis result. Also, if the user is relaxed, the analysis unit can provide a detailed analysis result. Furthermore, if the user is in a hurry, the analysis unit can provide a concise analysis result. In this way, the analysis unit can adjust the length of the analysis based on the user's emotion and provide an analysis result of an appropriate length for the user. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the analysis unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the analysis unit may input user emotion data into the generation AI and adjust the length of the analysis.

[0076] The analysis unit can determine the analysis priority based on the time when the data was collected during analysis. The analysis unit can determine the analysis priority based on the time when the data was collected during analysis, for example, using the generation AI. The analysis unit can, for example, prioritize analyzing the most recent data. The analysis unit can also analyze the data by emphasizing current data while referring to past data. The analysis unit can also analyze the data from a specific period by priority. For example, the analysis unit can analyze the most recent data by priority. The analysis unit can analyze the data by emphasizing current data while referring to past data. The analysis unit can also analyze the data from a specific period by priority. In this way, the analysis unit can prioritize analyzing the most recent data by determining the analysis priority based on the time when the data was collected. Some or all of the above-described processing in the analysis unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the analysis unit can input the time when the data was collected into the generation AI to determine the analysis priority.

[0077] The analysis unit can adjust the order of analysis based on the relevance of the data during analysis. The analysis unit can adjust the order of analysis based on the relevance of the data during analysis, for example, using a generation AI. The analysis unit can, for example, prioritize analysis of highly relevant data. The analysis unit can also postpone analysis of less relevant data. Furthermore, the analysis unit can dynamically adjust the order of analysis according to the relevance of the data. For example, the analysis unit prioritizes analysis of highly relevant data. The analysis unit can also postpone analysis of less relevant data. Furthermore, the analysis unit can dynamically adjust the order of analysis according to the relevance of the data. In this way, the analysis unit can perform efficient analysis by adjusting the order of analysis based on the relevance of the data. Some or all of the above-mentioned processing in the analysis unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the analysis unit can input the relevance of the data into the generation AI and adjust the order of analysis.

[0078] The suggestion unit can estimate the user's emotion and adjust the way the suggestion is expressed based on the estimated user's emotion. The suggestion unit can estimate the user's emotion using, for example, a generation AI and adjust the way the suggestion is expressed based on the estimated emotion. For example, when the user is stressed, the suggestion unit can provide a simple, highly visible suggestion. Furthermore, when the user is relaxed, the suggestion unit can provide a detailed suggestion. Furthermore, when the user is in a hurry, the suggestion unit can provide a suggestion that focuses on the main points. For example, when the user is stressed, the suggestion unit can provide a simple, highly visible suggestion. Furthermore, when the user is relaxed, the suggestion unit can provide a detailed suggestion. Furthermore, when the user is in a hurry, the suggestion unit can provide a suggestion that focuses on the main points. In this way, the suggestion unit can adjust the way the suggestion is expressed based on the user's emotion, thereby providing a suggestion that is easy for the user to understand. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the suggestion unit may be performed using or without the generation AI. For example, the suggestion unit may input user emotion data into the generation AI and adjust the way the suggestion is expressed.

[0079] The suggestion unit can adjust the level of detail of the proposal based on the importance of the action when making the suggestion. The suggestion unit can adjust the level of detail of the proposal based on the importance of the action when making the suggestion, for example, using a generation AI. The suggestion unit can, for example, make a detailed suggestion for an action with high importance. The suggestion unit can also make a simplified suggestion for an action with low importance. Furthermore, the suggestion unit can make a moderately detailed suggestion for an action with medium importance. For example, the suggestion unit can make a detailed suggestion for an action with high importance. The suggestion unit can also make a simplified suggestion for an action with low importance. Furthermore, the suggestion unit can make a moderately detailed suggestion for an action with medium importance. In this way, the suggestion unit can make an efficient suggestion by adjusting the level of detail of the suggestion based on the importance of the action. Some or all of the above-mentioned processing in the suggestion unit may be performed using the generation AI or may be performed without using the generation AI. For example, the suggestion unit can input the importance of the action to the generation AI and adjust the level of detail of the suggestion.

[0080] The suggestion unit can apply different suggestion algorithms depending on the category of the action when making a suggestion. For example, the suggestion unit can use a generation AI to apply different suggestion algorithms depending on the category of the action when making a suggestion. For example, the suggestion unit can apply an energy efficiency suggestion algorithm to an energy-saving action. The suggestion unit can also apply an emission reduction suggestion algorithm to a CO2 reduction action. Furthermore, the suggestion unit can apply a behavior improvement suggestion algorithm to a usage pattern improvement action. For example, the suggestion unit can apply an energy efficiency suggestion algorithm to an energy-saving action. The suggestion unit can also apply an emission reduction suggestion algorithm to a CO2 reduction action. Furthermore, the suggestion unit can apply a behavior improvement suggestion algorithm to a usage pattern improvement action. In this way, the suggestion unit can provide appropriate suggestion results by applying different suggestion algorithms depending on the category of the action. Some or all of the above-mentioned processing in the suggestion unit may be performed using or without the generation AI. For example, the suggestion unit can input the category of the action to the generation AI and apply different suggestion algorithms.

[0081] The suggestion unit can estimate the user's emotion and adjust the length of the suggestion based on the estimated user's emotion. The suggestion unit can estimate the user's emotion using, for example, a generation AI and adjust the length of the suggestion based on the estimated emotion. For example, when the user is stressed, the suggestion unit can provide a short and to-the-point suggestion. Furthermore, when the user is relaxed, the suggestion unit can provide a detailed suggestion. Furthermore, when the user is in a hurry, the suggestion unit can provide a concise suggestion. For example, when the user is stressed, the suggestion unit can provide a short and to-the-point suggestion. Furthermore, when the user is relaxed, the suggestion unit can provide a detailed suggestion. Furthermore, when the user is in a hurry, the suggestion unit can provide a concise suggestion. In this way, the suggestion unit can adjust the length of the suggestion based on the user's emotion, thereby providing a suggestion of an appropriate length for the user. The emotion estimation is realized using, for example, an emotion engine or a generation AI using an emotion estimation function. The generation AI can be, for example, 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 suggestion unit may be performed using or without the generation AI. For example, the suggestion unit may input user emotion data into the generation AI and adjust the length of the suggestion.

[0082] The suggestion unit can determine the priority of the suggestions based on the execution time of the actions when making the suggestions. The suggestion unit can determine the priority of the suggestions based on the execution time of the actions when making the suggestions, for example, using a generation AI. The suggestion unit can, for example, prioritize suggestions of actions that can be executed immediately. The suggestion unit can also postpone suggestions of actions that will be executed in the long term. Furthermore, the suggestion unit can dynamically adjust the priority of the suggestions according to the execution time of the actions. For example, the suggestion unit prioritizes suggestions of actions that can be executed immediately. The suggestion unit can also postpone suggestions of actions that will be executed in the long term. Furthermore, the suggestion unit can dynamically adjust the priority of the suggestions according to the execution time of the actions. In this way, the suggestion unit can make efficient suggestions by determining the priority of the suggestions based on the execution time of the actions. Some or all of the above-described processing in the suggestion unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the suggestion unit can input the execution time of the actions to the generation AI and determine the priority of the suggestions.

[0083] The suggestion unit can adjust the order of suggestions based on the relevance of actions when making suggestions. The suggestion unit can adjust the order of suggestions based on the relevance of actions when making suggestions, for example, using a generation AI. The suggestion unit can, for example, preferentially suggest highly relevant actions. The suggestion unit can also postpone suggesting less relevant actions. Furthermore, the suggestion unit can dynamically adjust the order of suggestions according to the relevance of actions. For example, the suggestion unit preferentially suggests highly relevant actions. The suggestion unit can also postpone suggesting less relevant actions. Furthermore, the suggestion unit can dynamically adjust the order of suggestions according to the relevance of actions. As a result, the suggestion unit can make efficient suggestions by adjusting the order of suggestions based on the relevance of actions. Some or all of the above-described processing in the suggestion unit may be performed using or without the generation AI. For example, the suggestion unit can input the relevance of actions to the generation AI and adjust the order of suggestions.

[0084] The evaluation unit can estimate the user's emotion and adjust the evaluation method based on the estimated user emotion. The evaluation unit can estimate the user's emotion using, for example, a generation AI and adjust the evaluation method based on the estimated emotion. For example, if the user is stressed, the evaluation unit can provide a simple, highly visible evaluation method. Furthermore, if the user is relaxed, the evaluation unit can provide a detailed evaluation method. Furthermore, if the user is in a hurry, the evaluation unit can provide a more concise evaluation method. For example, if the user is stressed, the evaluation unit can provide a simple, highly visible evaluation method. Furthermore, if the user is relaxed, the evaluation unit can provide a more concise evaluation method. In this way, the evaluation unit can adjust the evaluation method based on the user's emotion, thereby providing an evaluation result that is easy for the user to understand. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the evaluation unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the evaluation unit may input user emotion data into the generation AI and adjust the evaluation method.

[0085] The evaluation unit can analyze the user's past action history and select the optimal evaluation method during evaluation. For example, the evaluation unit uses a generation AI to analyze the user's past action history and select the optimal evaluation method during evaluation. The evaluation unit can perform evaluation based on the effects of actions previously performed by the user. The evaluation unit can also select optimal evaluation criteria from the user's past action history. Furthermore, the evaluation unit can evaluate the current action while referring to the user's past action history. For example, the evaluation unit performs evaluation based on the effects of actions previously performed by the user. The evaluation unit can select optimal evaluation criteria from the user's past action history. Furthermore, the evaluation unit can evaluate the current action while referring to the user's past action history. In this way, the evaluation unit can select the optimal evaluation method by analyzing the user's past action history. Some or all of the above-mentioned processing in the evaluation unit may be performed using the generation AI or may be performed without using the generation AI. For example, the evaluation unit can input the user's past action history into the generation AI and select the optimal evaluation method.

[0086] The evaluation unit can customize the evaluation means based on the user's current living situation during evaluation. The evaluation unit, for example, uses a generation AI to customize the evaluation means based on the user's current living situation during evaluation. For example, if the user is at work, the evaluation unit can provide a work-related evaluation method. Also, if the user is on vacation, the evaluation unit can provide a vacation-related evaluation method. Furthermore, if the user is interested in health, the evaluation unit can provide a health-related evaluation method. For example, if the user is at work, the evaluation unit can provide a work-related evaluation method. Also, if the user is on vacation, the evaluation unit can provide a vacation-related evaluation method. Furthermore, if the user is interested in health, the evaluation unit can provide a health-related evaluation method. In this way, the evaluation unit can provide an appropriate evaluation result by customizing the evaluation means based on the user's living situation. Some or all of the above-mentioned processing in the evaluation unit may be performed using or without the generation AI. For example, the evaluation unit can input the user's current living situation into the generation AI to customize the evaluation means.

[0087] The evaluation unit can estimate the user's emotions and determine the priority of evaluations based on the estimated user emotions. The evaluation unit, for example, uses a generation AI to estimate the user's emotions and determine the priority of evaluations based on the estimated emotions. For example, if the user is feeling stressed, the evaluation unit can prioritize evaluations that help reduce stress. Furthermore, if the user is relaxed, the evaluation unit can prioritize evaluations that help maintain relaxation. Furthermore, if the user is in a hurry, the evaluation unit can prioritize evaluations that help save time. For example, if the user is feeling stressed, the evaluation unit can prioritize evaluations that help reduce stress. Furthermore, if the user is relaxed, the evaluation unit can prioritize evaluations that help maintain relaxation. Furthermore, if the user is in a hurry, the evaluation unit can prioritize evaluations that help save time. In this way, the evaluation unit can prioritize evaluations that are important to the user by determining the priority of evaluations based on the user's emotions. Emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the evaluation unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the evaluation unit may input user emotion data into the generation AI and determine the priority of the evaluation.

[0088] The evaluation unit can select the optimal evaluation method by taking into account the user's geographical location information during evaluation. For example, the evaluation unit uses a generation AI to select the optimal evaluation method by taking into account the user's geographical location information during evaluation. For example, if the user is in a specific area, the evaluation unit can provide a evaluation method related to that area. Also, if the user is traveling, the evaluation unit can provide a evaluation method related to the travel destination. Furthermore, if the user is at home, the evaluation unit can provide a evaluation method for the area around the user's home. For example, if the user is in a specific area, the evaluation unit can provide a evaluation method related to that area. Also, if the user is traveling, the evaluation unit can provide a evaluation method related to the travel destination. Furthermore, if the user is at home, the evaluation unit can provide a evaluation method for the area around the user's home. This allows the evaluation unit to select the optimal evaluation method by taking into account the user's geographical location information. Some or all of the above-mentioned processing in the evaluation unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the evaluation unit can input the user's geographical location information into the generation AI and select the optimal evaluation method.

[0089] The evaluation unit can analyze the user's social media activity and suggest evaluation measures during evaluation. The evaluation unit can, for example, use a generation AI to analyze the user's social media activity and suggest evaluation measures during evaluation. The evaluation unit can, for example, provide evaluation methods related to topics in which the user has shown interest on social media. The evaluation unit can also suggest evaluation methods based on the activity of accounts the user follows. Furthermore, the evaluation unit can provide evaluation methods related to content posted by the user. For example, the evaluation unit can provide evaluation methods related to topics in which the user has shown interest on social media. The evaluation unit can also suggest evaluation methods based on the activity of accounts the user follows. Furthermore, the evaluation unit can provide evaluation methods related to content posted by the user. In this way, the evaluation unit can suggest related evaluation measures by analyzing the user's social media activity. Some or all of the above-mentioned processing in the evaluation unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the evaluation unit can input the user's social media activity into the generation AI and suggest evaluation measures. === Hard Collateral 1-1 === Each of the multiple elements including the collection unit, analysis unit, proposal unit, and evaluation unit described above is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the collection unit accesses a mobile carrier's database via the communication I / F 44 of the smart device 14 to acquire data. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the collected data. The proposal unit is realized by the control unit 46A of the smart device 14 and proposes actions for energy conservation and CO2 reduction based on the analysis results. The evaluation unit is realized by the specific processing unit 290 of the data processing device 12 and evaluates the effectiveness of the proposed actions and awards points. === Hard Collateral 1-2 === Each of the multiple elements, including the collection unit, analysis unit, suggestion unit, and evaluation unit, described above, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the collection unit accesses a mobile carrier's database via the communication I / F 44 of the smart glasses 214 to acquire data. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the collected data. The suggestion unit is realized by the control unit 46A of the smart glasses 214 and suggests actions for energy conservation and CO2 reduction based on the analysis results. The evaluation unit is realized by the specific processing unit 290 of the data processing device 12 and evaluates the effectiveness of the suggested actions and awards points. === Hard Collateral 1-3 === Each of the multiple elements including the collection unit, analysis unit, proposal unit, and evaluation unit described above is realized, for example, by at least one of the headset type terminal 314 and the data processing device 12. For example, the collection unit accesses a mobile carrier's database via the communication I / F 44 of the headset type terminal 314 to acquire data. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the collected data. The proposal unit is realized by the control unit 46A of the headset type terminal 314 and proposes actions for energy conservation and CO2 reduction based on the analysis results. The evaluation unit is realized by the specific processing unit 290 of the data processing device 12 and evaluates the effectiveness of the proposed actions and awards points. === Hard Collateral 1-4 === Each of the multiple elements including the collection unit, analysis unit, proposal unit, and evaluation unit described above is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the collection unit accesses a mobile carrier's database via the communication I / F 44 of the robot 414 to acquire data. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the collected data. The proposal unit is realized by the control unit 46A of the robot 414 and proposes actions for energy conservation and CO2 reduction based on the analysis results. The evaluation unit is realized by the specific processing unit 290 of the data processing device 12 and evaluates the effectiveness of the proposed actions and awards points.

[0090] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0091] The analysis unit can estimate the user's emotions and determine the analysis priority based on the estimated emotions. For example, if the user is feeling stressed, data useful for stress reduction can be prioritized for analysis. Also, if the user is relaxed, data for maintaining relaxation can be prioritized for analysis. Furthermore, if the user is in a hurry, data useful for saving time can be prioritized for analysis. Thus, by determining the analysis priority based on the user's emotions, the analysis unit can prioritize the analysis of data important to the user. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the analysis unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the analysis unit can input the user's emotion data into the generation AI and determine the analysis priority.

[0092] The suggestion unit can estimate the user's emotions and adjust the timing of suggestions based on the estimated emotions. For example, if the user is feeling stressed, the suggestion unit can reduce the frequency of suggestions to reduce the user's burden. Furthermore, if the user is relaxed, the suggestion unit can increase the frequency of suggestions and provide more detailed suggestions. Furthermore, if the user is in a hurry, the suggestion unit can temporarily stop suggestions and resume them later. This allows the suggestion unit to adjust the timing of suggestions based on the user's emotions, thereby reducing the user's burden and providing effective suggestions. The emotion estimation is realized using an emotion estimation function, such as 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 suggestion unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the suggestion unit can input the user's emotion data into the generation AI and adjust the timing of suggestions.

[0093] The evaluation unit can estimate the user's emotions and adjust the evaluation feedback method based on the estimated emotions. For example, if the user is feeling stressed, simple, highly visible feedback can be provided. If the user is relaxed, detailed feedback can be provided. If the user is in a hurry, feedback that focuses on the main points can be provided. By adjusting the evaluation feedback method based on the user's emotions, the evaluation unit can 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 generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the evaluation unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the evaluation unit can input the user's emotion data into the generation AI and adjust the evaluation feedback method.

[0094] The collection unit can estimate the user's emotions and adjust the data collection method 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. 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. This allows the collection unit to adjust the data collection method based on the user's emotions, reducing the user's burden and enabling effective data collection. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the collection unit can be performed using the generation AI, or can be performed without using the generation AI. For example, the collection unit can input the user's emotion data into the generation AI and adjust the data collection method.

[0095] The suggestion unit can estimate the user's emotions and adjust the content of the suggestions based on the estimated emotions. For example, if the user is feeling stressed, the suggestion unit can provide simple and easy-to-implement suggestions. If the user is relaxed, the suggestion unit can provide detailed and complex suggestions. Furthermore, if the user is in a hurry, the suggestion unit can provide suggestions that are easy to implement for the user by adjusting the content of the suggestions based on the user's emotions. The estimation of emotions is realized using an emotion estimation function, such as 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 suggestion unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the suggestion unit can input the user's emotion data into the generation AI and adjust the content of the suggestions.

[0096] The collection unit can analyze the user's past data collection history and select the optimal data collection method. For example, it can prioritize collection of data from apps that the user frequently used in the past. It can also select the optimal data collection time period based on the user's past usage patterns. Furthermore, it can suggest an efficient data collection method based on the user's past data collection history. In this way, the collection unit can select the optimal data collection method by analyzing the user's past usage history. Some or all of the above-mentioned processing in the collection unit may be performed using or without the generation AI. For example, the collection unit can input the user's past mobile phone usage history into the generation AI and select the optimal data collection method.

[0097] The analysis unit can adjust the level of detail of the analysis based on the importance of the data. For example, a detailed analysis can be performed on data with high importance. A simplified analysis can be performed on data with low importance. Furthermore, an analysis with an appropriate level of detail can be performed on data with medium importance. In this way, the analysis unit can perform an efficient analysis by adjusting the level of detail of the analysis based on the importance of the data. Some or all of the above-described processing in the analysis unit may be performed using the generation AI, or may be performed without using the generation AI. For example, the analysis unit can input the importance of the data into the generation AI and adjust the level of detail of the analysis.

[0098] The suggestion unit can adjust the level of detail of the suggestion based on the importance of the action when making a suggestion. For example, a detailed suggestion can be made for an action with high importance. A simplified suggestion can be made for an action with low importance. Furthermore, a suggestion with an appropriate level of detail can be made for an action with medium importance. In this way, the suggestion unit can make an efficient suggestion by adjusting the level of detail of the suggestion based on the importance of the action. Some or all of the above-described processing in the suggestion unit may be performed using or without using the generation AI. For example, the suggestion unit can input the importance of the action to the generation AI and adjust the level of detail of the suggestion.

[0099] During evaluation, the evaluation unit can analyze the user's past action history to select the optimal evaluation method. For example, the evaluation can be performed based on the effects of actions performed by the user in the past. The evaluation unit can also select optimal evaluation criteria from the user's past action history. Furthermore, the current action can be evaluated while referring to the user's past action history. This allows the evaluation unit to select the optimal evaluation method by analyzing the user's past action history. Some or all of the above-mentioned processing in the evaluation unit may be performed using or without the generation AI. For example, the evaluation unit can input the user's past action history into the generation AI to select the optimal evaluation method.

[0100] When collecting data, the collection unit can prioritize collecting highly relevant data by taking into account the user's geographical location information. For example, when the user is in a specific area, data related to that area can be prioritized. Also, when the user is traveling, data related to the travel destination can be prioritized. Furthermore, when the user is at home, data around the home can be prioritized. In this way, the collection unit can prioritize collecting highly relevant data by taking into account the user's geographical location information. Some or all of the above-mentioned processing in the collection unit may be performed using or without the generation AI. For example, when collecting data, the collection unit can input the user's geographical location information into the generation AI and prioritize collecting highly relevant data.

[0101] The processing flow of the second embodiment will be briefly explained below.

[0102] Step 1: The collection unit collects mobile phone usage data. For example, the collection unit accesses the mobile phone carrier's database or system and obtains data through APIs or collaboration. The collection unit can collect data such as call history, app usage time, and data traffic. Step 2: The analysis unit analyzes the collected data and evaluates the user's mobile phone usage patterns and energy consumption status. The analysis unit analyzes the data using, for example, generative AI and evaluates the user's usage patterns and energy consumption status in real time. The analysis unit can, for example, analyze the user's call frequency and app usage time to understand energy consumption trends. Step 3: The suggestion unit proposes specific actions to save energy and reduce CO2 emissions based on the analysis results. For example, the suggestion unit uses a generative AI to propose actions to save energy and reduce CO2 emissions based on the analysis results. For example, the suggestion unit can suggest to the user that they use a power-saving mode or delete unnecessary apps. Step 4: The evaluation unit evaluates the effectiveness of the proposed action and awards points. The evaluation unit, for example, uses a generation AI to evaluate the effectiveness of the proposed action and award points. For example, the evaluation unit evaluates the effectiveness of the action performed by the user and awards points, thereby increasing the user's motivation to participate.

[0103] 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.

[0104] 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 the generative AI include a neural network (NN) and a neural network (NN). 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 (e.g., still image data or video data). 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 one or more data formats of voice data, text data, image data, etc. 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 may perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-mentioned parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. The processing performed by an AI including the generative AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI including the generative AI.

[0105] 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.

[0106] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0107] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0108] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] 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).

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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 including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). 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 one or more data formats, such as audio data, text data, and image 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 models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0121] 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.

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

[0123] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0124] 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.

[0125] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0126] The 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.

[0127] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0128] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0129] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0130] Fig. 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.

[0131] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0132] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0133] In the 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.

[0134] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0135] The specific processing unit 290 transmits the result of the specific processing to the 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.

[0136] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). 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 one or more data formats, such as audio data, text data, and image 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 models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0137] 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.

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

[0139] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0140] 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.

[0141] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0142] The 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.

[0143] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0144] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS 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).

[0145] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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 including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). 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 one or more data formats, such as audio data, text data, and image 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 models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0154] 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.

[0155] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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).

[0160] 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 indicated, and when they approach the ideal, a state of pleasure is indicated. 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 indicated, and when they approach the ideal, a state of pleasure is indicated. 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.

[0161] 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."

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] [Explanation of symbols]

[0175] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a collection unit that collects data; an analysis unit that analyzes the data collected by the collection unit; a proposal unit that makes a proposal based on the analysis result obtained by the analysis unit; an evaluation unit that evaluates the effectiveness of the action proposed by the proposal unit. A system characterized by:

2. The collecting unit Collecting mobile phone usage data 2. The system of claim 1.

3. The analysis unit Analyze the collected data to assess users' mobile phone usage patterns and energy consumption.

2. The system of claim 1.

4. The proposal unit Propose specific actions for energy conservation and CO2 reduction based on the analysis results 2. The system of claim 1.

5. The evaluation unit Evaluate the effectiveness of the proposed actions and reward points 2. The system of claim 1.

6. The proposal unit Compare with other users 2. The system of claim 1.

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

8. The collecting unit Analyze users' past mobile phone usage history and select the optimal data collection method 2. The system of claim 1.

Images