system

Abstract

The system according to the embodiment aims to predict changes in the global environment and propose and implement specific countermeasures. [Solution] A system according to an embodiment includes a collection unit, an analysis unit, a prediction unit, a proposal unit, an execution unit, and an evaluation unit. The collection unit collects data related to the global environment. The analysis unit analyzes the data collected by the collection unit. The prediction unit predicts changes in the global environment based on the data analyzed by the analysis unit. The proposal unit proposes specific countermeasures based on the results predicted by the prediction unit. The execution unit implements the countermeasures proposed by the proposal unit. The evaluation unit evaluates the effectiveness of the countermeasures implemented by the execution unit.

Description

[Technical Field]

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

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

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

[0004] With conventional technology, the process of predicting changes in the global environment and proposing and implementing specific countermeasures is not sufficiently efficient, and there is room for improvement.

[0005] The system according to the embodiment aims to predict changes in the global environment and propose and implement specific countermeasures. [Means for solving the problem]

[0006] The system according to the embodiment includes a collection unit, an analysis unit, a prediction unit, a proposal unit, an execution unit, and an evaluation unit. The collection unit collects data related to the global environment. The analysis unit analyzes the data collected by the collection unit. The prediction unit predicts changes in the global environment based on the data analyzed by the analysis unit. The proposal unit proposes specific countermeasures based on the results predicted by the prediction unit. The execution unit implements the countermeasures proposed by the proposal unit. The evaluation unit evaluates the effectiveness of the countermeasures implemented by the execution unit. [Effects of the Invention]

[0007] The system according to the embodiment can predict changes in the global environment and propose and implement specific countermeasures. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) The environmental sharing and improvement system according to an embodiment of the present invention utilizes AI technology to predict and address global issues. This system collects data related to the global environment, analyzes it using AI, grasps the current state of the global environment, and predicts future changes in the global environment. For example, data such as temperature, precipitation, CO2 concentration, and deforestation rate are collected and analyzed by AI. Furthermore, the AI ​​compares the collected data with past data to predict future changes in the global environment. For example, it predicts increases in temperature, changes in precipitation, and increases in CO2 concentration. Based on the predicted results, the system proposes specific countermeasures, such as methods to reduce CO2 emissions, promoting forest conservation activities, and promoting the use of renewable energy. These proposals are provided to users via familiar tools such as smartphones, tablets, and train station touchscreens. Users can review and implement the proposed countermeasures. For example, to reduce CO2 emissions, users can take specific actions such as reviewing their energy consumption at home, using public transportation, and participating in recycling activities. The system can also record users' actions and evaluate their effectiveness. This allows users to see how their actions affect the global environment. This system makes it possible to grasp the current state of the global environment, predict future changes, and propose specific countermeasures. Furthermore, users can easily check and respond at any time using familiar tools, thereby contributing to improving the global environment. In this way, the Environmental Sharing and Improvement System can grasp the current state of the global environment, predict future changes, propose specific countermeasures, implement them, and evaluate their effectiveness.

[0029] The environmental sharing and improvement system according to the embodiment includes a collection unit, an analysis unit, a prediction unit, a proposal unit, an execution unit, and an evaluation unit. The collection unit collects data related to the global environment. For example, the collection unit collects data such as temperature, precipitation, CO2 concentration, and deforestation rate. The collection unit, for example, uses a temperature sensor to collect temperature data. It can also use a precipitation sensor to collect precipitation data. It can also use a CO2 sensor to collect CO2 concentration data. For example, the collection unit installs a temperature sensor and periodically collects temperature data. It can also install a precipitation sensor and collect precipitation data. It can also install a CO2 sensor and collect CO2 concentration data. The analysis unit analyzes the data collected by the collection unit. For example, the analysis unit analyzes the data using statistical analysis. It can also analyze the data using a machine learning algorithm. For example, the analysis unit calculates the average value of the temperature data using statistical analysis. It can also analyze fluctuations in the CO2 concentration data using a machine learning algorithm. The prediction unit predicts changes in the global environment based on the data analyzed by the analysis unit. For example, the prediction unit predicts future temperature changes by comparing the data with past data. The system can also predict future changes in precipitation using a simulation model. For example, the prediction unit predicts future temperature increases based on temperature data from the past 10 years. The system can also predict future changes in precipitation using a simulation model. The proposal unit proposes specific countermeasures based on the results predicted by the prediction unit. For example, the proposal unit proposes a method for reducing CO2 emissions. The proposal unit can also propose promoting forest conservation activities. For example, the proposal unit can propose improving energy efficiency. The implementation unit can also propose promoting reforestation activities. The implementation unit implements the countermeasures proposed by the proposal unit. For example, the implementation unit implements a CO2 reduction plan. The system can also implement forest conservation activities. For example, the implementation unit can implement improving energy efficiency. The implementation unit can also implement reforestation activities. The evaluation unit evaluates the effectiveness of the countermeasures implemented by the implementation unit. For example, the evaluation unit records user behavior and evaluates the effectiveness. For example, the evaluation unit collects behavior logs and evaluates the effectiveness.Furthermore, the effectiveness can be evaluated using evaluation indicators. This allows the environmental sharing and improvement system according to the embodiment to grasp the current state of the global environment, predict future changes, propose and implement specific countermeasures, and evaluate their effectiveness.

[0030] The collection unit can collect data on temperature, precipitation, CO2 concentration, and deforestation rate. The collection unit, for example, collects temperature data using a temperature sensor. For example, the collection unit installs a temperature sensor and periodically collects temperature data. The collection unit can also collect precipitation data using a precipitation sensor. For example, the collection unit installs a precipitation sensor and collects precipitation data. The collection unit can also collect CO2 concentration data using a CO2 sensor. For example, the collection unit installs a CO2 sensor and collects CO2 concentration data. The collection unit can also analyze satellite images to measure the deforestation rate. For example, the collection unit acquires satellite images and calculates the deforestation rate using image analysis technology. This allows the collection unit to collect a variety of data related to the global environment.

[0031] The analysis unit analyzes the collected data and can grasp the current state of the global environment. The analysis unit analyzes the data using, for example, statistical analysis. For example, the analysis unit calculates the average value of temperature data. The analysis unit can also analyze the data using a machine learning algorithm. For example, the analysis unit analyzes fluctuations in CO2 concentration data. The analysis unit can also analyze correlations in the data. For example, the analysis unit analyzes the correlation between temperature and precipitation. This allows the analysis unit to accurately grasp the current state of the global environment.

[0032] The prediction unit can predict future changes in the global environment by comparing it with past data. For example, the prediction unit can predict future temperature changes by comparing them with past data. For example, the prediction unit can predict future temperature increases based on temperature data from the past 10 years. The prediction unit can also predict future changes in precipitation using simulation models. For example, the prediction unit can predict future changes in precipitation using simulation models. The prediction unit can also predict increases in CO2 concentration. For example, the prediction unit can predict future increases in CO2 concentration based on past CO2 concentration data. In this way, the prediction unit can predict future changes in the global environment.

[0033] Based on the predicted results, the proposal committee can propose specific measures to reduce CO2 emissions, promote forest conservation activities, and encourage the use of renewable energy. For example, the proposal committee can propose methods to reduce CO2 emissions. For example, the proposal committee can propose improving energy efficiency. The proposal committee can also propose the use of renewable energy. For example, the proposal committee can propose the introduction of solar power generation. The proposal committee can also propose promoting forest conservation activities. For example, the proposal committee can propose promoting afforestation activities. The proposal committee can also propose preventing illegal logging. For example, the proposal committee can propose monitoring for illegal logging. This allows the proposal committee to propose specific countermeasures.

[0034] The implementing body can carry out the proposed countermeasures. For example, the implementing body can implement a CO2 reduction plan. For example, the implementing body can implement energy efficiency improvements. The implementing body can also implement the use of renewable energy. For example, the implementing body can implement the introduction of solar power generation. The implementing body can also carry out forest conservation activities. For example, the implementing body can carry out afforestation activities. The implementing body can also carry out measures to prevent illegal logging. For example, the implementing body can monitor illegal logging. In this way, the implementing body can carry out the proposed countermeasures.

[0035] The evaluation unit can record the user's actions and evaluate the effects. The evaluation unit, for example, records the user's actions. For example, the evaluation unit collects action logs. The evaluation unit can also evaluate the effects. For example, the evaluation unit evaluates the effects using evaluation indices. For example, the evaluation unit evaluates the effects of reducing CO2 emissions. The evaluation unit can also evaluate the effects of forest protection activities. For example, the evaluation unit evaluates the effects of reforestation activities. This allows the evaluation unit to record the user's actions and evaluate their effects.

[0036] The collection unit can analyze past data collection history and select the optimal collection method. The collection unit, for example, selects the most efficient collection method from the past data collection history. For example, the collection unit analyzes the past data collection history and selects the most efficient collection method. The collection unit can also optimize the collection frequency based on the past data collection history. For example, the collection unit analyzes the past data collection history and optimizes the collection frequency. The collection unit can also analyze the past data collection history and determine the priority of collection targets. For example, the collection unit analyzes the past data collection history and determines the priority of collection targets. This allows the collection unit to select the optimal collection method based on the past data collection history.

[0037] The collection unit can filter data based on specific environmental conditions or events when collecting data. For example, the collection unit collects data only when the temperature is above a certain level. For example, the collection unit collects data only when the temperature is above a certain level. The collection unit can also collect data when a specific event (e.g., a forest fire) occurs. For example, the collection unit collects data when a forest fire occurs. The collection unit can also collect data only when the CO2 concentration is above a certain level. For example, the collection unit collects data only when the CO2 concentration is above a certain level. This allows the collection unit to filter data based on specific environmental conditions or events.

[0038] When collecting data, the collection unit can prioritize collecting highly relevant data by taking geographical location information into consideration. The collection unit, for example, prioritizes collecting temperature data from a specific region. For example, the collection unit prioritizes collecting temperature data from a specific region by taking geographical location information into consideration. The collection unit can also prioritize collecting data from regions with high CO2 concentrations based on the geographical location information. For example, the collection unit prioritizes collecting data from regions with high CO2 concentrations by taking geographical location information into consideration. The collection unit can also prioritize collecting data from regions with high deforestation rates by taking geographical location information into consideration. For example, the collection unit prioritizes collecting data from regions with high deforestation rates by taking geographical location information into consideration. This allows the collection unit to prioritize collecting highly relevant data based on the geographical location information.

[0039] The collection unit can analyze social media activities and collect related data when collecting data. For example, the collection unit collects data on environmental issues that are trending on social media. For example, the collection unit collects data on environmental issues that are trending on social media. The collection unit can also analyze posts on social media and collect environmental data for a specific region. For example, the collection unit analyzes posts on social media and collects environmental data for a specific region. The collection unit can also collect related environmental data based on trends on social media. For example, the collection unit collects related environmental data based on trends on social media. In this way, the collection unit can collect related data based on social media activities.

[0040] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the data. The analysis unit, for example, performs a detailed analysis on data with high importance. For example, the analysis unit evaluates the importance of the data and performs a detailed analysis on the data with high importance. The analysis unit can also perform a simplified analysis on data with low importance. For example, the analysis unit evaluates the importance of the data and performs a simplified analysis on the data with low importance. The analysis unit can also determine the priority of the analysis based on the importance of the data. For example, the analysis unit evaluates the importance of the data and determines the priority of the analysis. This allows the analysis unit to adjust the level of detail of the analysis based on the importance of the data.

[0041] During analysis, the analysis unit can apply different analysis algorithms depending on the category of data. For example, the analysis unit applies a specific analysis algorithm to temperature data. For example, the analysis unit applies a specific analysis algorithm to temperature data. The analysis unit can also apply a different analysis algorithm to CO2 concentration data. For example, the analysis unit applies a different analysis algorithm to CO2 concentration data. The analysis unit can also apply a dedicated analysis algorithm to deforestation rate data. For example, the analysis unit applies a dedicated analysis algorithm to deforestation rate data. This allows the analysis unit to apply an appropriate analysis algorithm depending on the category of data.

[0042] During analysis, the analysis unit can determine the order of analysis priorities based on the time when the data was collected. The analysis unit, for example, prioritizes analyzing the most recent data. For example, the analysis unit prioritizes analyzing the most recent data, taking into account the time when the data was collected. The analysis unit can also analyze trends based on past data. For example, the analysis unit analyzes trends based on past data. The analysis unit can also determine the order of analysis based on the time when the data was collected. For example, the analysis unit decides the order of analysis, taking into account the time when the data was collected. This allows the analysis unit to determine the order of analysis priorities based on the time when the data was collected.

[0043] During analysis, the analysis unit can adjust the order of analysis based on the relevance of the data. The analysis unit, for example, prioritizes analysis of highly relevant data. For example, the analysis unit evaluates the relevance of the data and prioritizes analysis of highly relevant data. The analysis unit can also postpone data with low relevance. For example, the analysis unit evaluates the relevance of the data and postpones analysis of data with low relevance. The analysis unit can also optimize the order of analysis based on the relevance of the data. For example, the analysis unit evaluates the relevance of the data and optimizes the order of analysis. This allows the analysis unit to adjust the order of analysis based on the relevance of the data.

[0044] The prediction unit can improve the accuracy of the prediction by taking into account the interrelationship between data when making a prediction. The prediction unit, for example, makes a prediction by taking into account the interrelationship between temperature and precipitation. For example, the prediction unit makes a prediction by taking into account the interrelationship between temperature and precipitation. The prediction unit can also make a prediction by taking into account the interrelationship between CO2 concentration and deforestation rate. For example, the prediction unit makes a prediction by taking into account the interrelationship between CO2 concentration and deforestation rate. The prediction unit can also improve the accuracy of the prediction based on the interrelationship between data. For example, the prediction unit improves the accuracy of the prediction based on the interrelationship between data. In this way, the prediction unit can improve the accuracy of the prediction by taking into account the interrelationship between data.

[0045] When making a prediction, the prediction unit can make a prediction taking into account attribute information of the data submitter. The prediction unit, for example, makes a prediction taking into account the specialized knowledge of the data submitter. For example, the prediction unit makes a prediction taking into account the specialized knowledge of the data submitter. The prediction unit can also make a prediction based on the past performance of the data submitter. For example, the prediction unit makes a prediction based on the past performance of the data submitter. The prediction unit can also improve the accuracy of the prediction based on the attribute information of the data submitter. For example, the prediction unit improves the accuracy of the prediction based on the attribute information of the data submitter. This allows the prediction unit to improve the accuracy of the prediction taking into account the attribute information of the data submitter.

[0046] The prediction unit can make predictions taking into account the geographical distribution of data when making predictions. The prediction unit, for example, makes predictions based on data from geographically close areas. For example, the prediction unit makes predictions based on data from geographically close areas. The prediction unit can also make predictions based on data from geographically distant areas. For example, the prediction unit makes predictions based on data from geographically distant areas. The prediction unit can also improve the accuracy of predictions by taking into account the geographical distribution. For example, the prediction unit improves the accuracy of predictions by taking into account the geographical distribution. This allows the prediction unit to improve the accuracy of predictions by taking into account the geographical distribution of data.

[0047] The prediction unit can improve the accuracy of prediction by referring to related literature during prediction. The prediction unit, for example, improves the accuracy of prediction based on related literature. For example, the prediction unit improves the accuracy of prediction based on related literature. The prediction unit can also adjust the prediction criteria by referring to related literature. For example, the prediction unit adjusts the prediction criteria by referring to related literature. The prediction unit can also complement the prediction result based on related literature. For example, the prediction unit complements the prediction result based on related literature. In this way, the prediction unit can improve the accuracy of prediction by referring to related literature.

[0048] The proposal unit can adjust the level of detail of the proposal based on the importance of the countermeasure when making a proposal. For example, the proposal unit makes a detailed proposal for a countermeasure with high importance. For example, the proposal unit evaluates the importance of the countermeasure and makes a detailed proposal for the countermeasure with high importance. The proposal unit can also make a simplified proposal for a countermeasure with low importance. For example, the proposal unit evaluates the importance of the countermeasure and makes a simplified proposal for the countermeasure with low importance. The proposal unit can also determine the priority of the proposal based on the importance of the countermeasure. For example, the proposal unit evaluates the importance of the countermeasure and determines the priority of the proposal. This allows the proposal unit to adjust the level of detail of the proposal based on the importance of the countermeasure.

[0049] When making a proposal, the proposal unit can apply different proposal algorithms depending on the category of the countermeasure. For example, the proposal unit applies a specific algorithm to proposals for reducing CO2 emissions. For example, the proposal unit applies a specific algorithm to proposals for reducing CO2 emissions. The proposal unit can also apply a different algorithm to proposals for forest protection activities. For example, the proposal unit applies a different algorithm to proposals for forest protection activities. The proposal unit can also apply a dedicated algorithm to proposals for promoting the use of renewable energy. For example, the proposal unit applies a dedicated algorithm to proposals for promoting the use of renewable energy. This allows the proposal unit to apply an appropriate proposal algorithm depending on the category of the countermeasure.

[0050] When making a proposal, the proposal unit can determine the priority of the proposals based on the time when the countermeasures were submitted. The proposal unit, for example, gives priority to proposing the most recent countermeasure. For example, the proposal unit gives priority to proposing the most recent countermeasure, taking into account the time when the countermeasures were submitted. The proposal unit can also propose trends based on past countermeasures. For example, the proposal unit can propose trends based on past countermeasures. The proposal unit can also determine the order of the proposals based on the time when the proposals were submitted. For example, the proposal unit determines the order of the proposals, taking into account the time when the countermeasures were submitted. This allows the proposal unit to determine the priority of the proposals based on the time when the countermeasures were submitted.

[0051] The proposal unit can adjust the order of proposals based on the relevance of the countermeasures when making proposals. The proposal unit, for example, prioritizes proposing highly relevant countermeasures. For example, the proposal unit evaluates the relevance of the countermeasures and prioritizes proposing highly relevant countermeasures. The proposal unit can also postpone less relevant countermeasures. For example, the proposal unit evaluates the relevance of the countermeasures and postpones less relevant countermeasures. The proposal unit can also optimize the order of proposals based on the relevance of the countermeasures. For example, the proposal unit evaluates the relevance of the countermeasures and optimizes the order of proposals. This allows the proposal unit to adjust the order of proposals based on the relevance of the countermeasures.

[0052] At the time of execution, the execution unit can analyze past execution history and select the optimal execution method. The execution unit, for example, selects the most efficient execution method from the past execution history. For example, the execution unit analyzes the past execution history and selects the most efficient execution method. The execution unit can also optimize the execution frequency based on the past execution history. For example, the execution unit analyzes the past execution history and optimizes the execution frequency. The execution unit can also analyze the past execution history and determine the priority of execution targets. For example, the execution unit analyzes the past execution history and determine the priority of execution targets. This allows the execution unit to select the optimal execution method based on the past execution history.

[0053] At the time of execution, the execution unit can customize the means of execution based on the current environmental conditions. The execution unit selects the optimal means of execution based on, for example, the current temperature. For example, the execution unit selects the optimal means of execution based on the current temperature. The execution unit can also customize the means of execution based on the current CO2 concentration. For example, the execution unit customizes the means of execution based on the current CO2 concentration. The execution unit can also adjust the means of execution based on the current rate of deforestation. For example, the execution unit adjusts the means of execution based on the current rate of deforestation. In this way, the execution unit can customize the means of execution based on the current environmental conditions.

[0054] The execution unit can select the optimal execution method at the time of execution by taking geographical location information into consideration. The execution unit, for example, selects the execution method based on data of a geographically close area. For example, the execution unit selects the execution method based on data of a geographically close area. The execution unit can also select the execution method based on data of a geographically distant area. For example, the execution unit selects the execution method based on data of a geographically distant area. The execution unit can also select the optimal execution method by taking geographical location information into consideration. For example, the execution unit selects the optimal execution method by taking geographical location information into consideration. This allows the execution unit to select the optimal execution method based on the geographical location information.

[0055] During execution, the execution unit can analyze social media activity and suggest action measures. For example, the execution unit suggests action measures related to environmental issues that are trending on social media. For example, the execution unit suggests action measures related to environmental issues that are trending on social media. The execution unit can also analyze posts on social media and suggest action measures for a specific region. For example, the execution unit can analyze posts on social media and suggest action measures for a specific region. The execution unit can also suggest related action measures based on trends on social media. For example, the execution unit suggests related action measures based on trends on social media. In this way, the execution unit can suggest related action measures based on social media activity.

[0056] The evaluation unit can optimize the evaluation algorithm by referring to past evaluation data during evaluation. The evaluation unit, for example, optimizes the evaluation algorithm based on past evaluation data. For example, the evaluation unit optimizes the evaluation algorithm based on past evaluation data. The evaluation unit can also adjust the evaluation criteria by referring to past evaluation data. For example, the evaluation unit adjusts the evaluation criteria by referring to past evaluation data. The evaluation unit can also improve the accuracy of the evaluation based on the past evaluation data. For example, the evaluation unit improves the accuracy of the evaluation based on the past evaluation data. This allows the evaluation unit to optimize the evaluation algorithm based on the past evaluation data.

[0057] During evaluation, the evaluation unit can weight the evaluation data based on the time when the data was collected. The evaluation unit, for example, weights the most recent data highly. For example, the evaluation unit takes into account the time when the data was collected and weights the most recent data highly. The evaluation unit can also weight older data lightly. For example, the evaluation unit takes into account the time when the data was collected and weights the older data lightly. The evaluation unit can also adjust the weighting of the evaluation data based on the time when the data was collected. For example, the evaluation unit adjusts the weighting of the evaluation data based on the time when the data was collected. This allows the evaluation unit to weight the evaluation data based on the time when the data was collected.

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

[0059] When analyzing data, the analysis unit can evaluate the reliability of the data and prioritize analysis of highly reliable data. For example, the analysis unit evaluates the source and collection method of the data and prioritizes analysis of highly reliable data. The analysis unit can also adjust the accuracy of the analysis results based on the reliability of the data. For example, the accuracy of the analysis results is set low for data with low reliability. Furthermore, the analysis unit can determine the priority of analysis based on the reliability of the data. This allows the analysis unit to adjust the accuracy and priority of analysis taking into account the reliability of the data.

[0060] The collection unit can analyze the user's behavioral patterns when collecting data and determine the optimal collection timing. For example, the collection unit collects data during times when the user is active. The collection unit can also refrain from collecting data during times when the user is resting. Furthermore, the collection unit can adjust the frequency of data collection based on the user's behavioral patterns. This allows the collection unit to determine the optimal data collection timing taking the user's behavioral patterns into consideration.

[0061] The prediction unit can detect outliers during prediction and remove the outliers to improve the accuracy of the prediction. For example, the prediction unit can detect and remove outliers in a dataset. The prediction unit can also correct the outliers to minimize the impact of the outliers. Furthermore, the prediction unit can adjust the prediction model based on the outlier detection result. This allows the prediction unit to improve the accuracy of the prediction by taking the outliers into account.

[0062] When making a suggestion, the suggestion unit can refer to the user's past behavioral history and make the most suitable suggestion to the user. For example, the suggestion unit analyzes the user's past behavioral history and makes a suggestion that is easy for the user to implement. The suggestion unit can also customize the content of the suggestion based on the user's past behavioral history. Furthermore, the suggestion unit can determine the priority of the suggestion taking into account the user's past behavioral history. This allows the suggestion unit to make the most suitable suggestion taking into account the user's past behavioral history.

[0063] The execution unit can collect user feedback during execution and improve its execution method. For example, the execution unit collects user feedback and improves its execution method. It can also adjust the execution procedure based on user feedback. Furthermore, the execution unit can determine execution priorities considering user feedback. This allows the execution unit to improve its execution method by reflecting user feedback.

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

[0065] Step 1: The collection unit collects data related to the global environment. For example, data on temperature, precipitation, CO2 concentration, deforestation rate, etc. The collection unit collects this data using temperature sensors, precipitation sensors, and CO2 sensors. Step 2: The analysis unit analyzes the data collected by the collection unit. The analysis unit analyzes the data using statistical analysis and machine learning algorithms. For example, the analysis unit calculates the average value of the temperature data and analyzes fluctuations in the CO2 concentration data. Step 3: The forecasting unit predicts changes in the global environment based on the data analyzed by the analysis unit. The forecasting unit compares data from the past to predict future changes in temperature, and uses simulation models to predict future changes in precipitation. Step 4: The Proposal Department proposes specific countermeasures based on the results predicted by the Forecasting Department, such as ways to reduce CO2 emissions and promote forest conservation activities. Step 5: The Implementation Department implements the measures proposed by the Proposal Department. The Implementation Department implements CO2 reduction plans and forest protection activities. Step 6: The evaluation unit evaluates the effectiveness of the countermeasures implemented by the execution unit. The evaluation unit collects user action logs and evaluates the effectiveness using evaluation indices.

[0066] (Example 2) The environmental sharing and improvement system according to an embodiment of the present invention utilizes AI technology to predict and address global issues. This system collects data related to the global environment, analyzes it using AI, grasps the current state of the global environment, and predicts future changes in the global environment. For example, data such as temperature, precipitation, CO2 concentration, and deforestation rate are collected and analyzed by AI. Furthermore, the AI ​​compares the collected data with past data to predict future changes in the global environment. For example, it predicts increases in temperature, changes in precipitation, and increases in CO2 concentration. Based on the predicted results, the system proposes specific countermeasures, such as methods to reduce CO2 emissions, promoting forest conservation activities, and promoting the use of renewable energy. These proposals are provided to users via familiar tools such as smartphones, tablets, and train station touchscreens. Users can review and implement the proposed countermeasures. For example, to reduce CO2 emissions, users can take specific actions such as reviewing their energy consumption at home, using public transportation, and participating in recycling activities. The system can also record users' actions and evaluate their effectiveness. This allows users to see how their actions affect the global environment. This system makes it possible to grasp the current state of the global environment, predict future changes, and propose specific countermeasures. Furthermore, users can easily check and respond at any time using familiar tools, thereby contributing to improving the global environment. In this way, the Environmental Sharing and Improvement System can grasp the current state of the global environment, predict future changes, propose specific countermeasures, implement them, and evaluate their effectiveness.

[0067] The environmental sharing and improvement system according to the embodiment includes a collection unit, an analysis unit, a prediction unit, a proposal unit, an execution unit, and an evaluation unit. The collection unit collects data related to the global environment. For example, the collection unit collects data such as temperature, precipitation, CO2 concentration, and deforestation rate. The collection unit, for example, uses a temperature sensor to collect temperature data. It can also use a precipitation sensor to collect precipitation data. It can also use a CO2 sensor to collect CO2 concentration data. For example, the collection unit installs a temperature sensor and periodically collects temperature data. It can also install a precipitation sensor and collect precipitation data. It can also install a CO2 sensor and collect CO2 concentration data. The analysis unit analyzes the data collected by the collection unit. For example, the analysis unit analyzes the data using statistical analysis. It can also analyze the data using a machine learning algorithm. For example, the analysis unit calculates the average value of the temperature data using statistical analysis. It can also analyze fluctuations in the CO2 concentration data using a machine learning algorithm. The prediction unit predicts changes in the global environment based on the data analyzed by the analysis unit. For example, the prediction unit predicts future temperature changes by comparing the data with past data. The system can also predict future changes in precipitation using a simulation model. For example, the prediction unit predicts future temperature increases based on temperature data from the past 10 years. The system can also predict future changes in precipitation using a simulation model. The proposal unit proposes specific countermeasures based on the results predicted by the prediction unit. For example, the proposal unit proposes a method for reducing CO2 emissions. The proposal unit can also propose promoting forest conservation activities. For example, the proposal unit can propose improving energy efficiency. The implementation unit can also propose promoting reforestation activities. The implementation unit implements the countermeasures proposed by the proposal unit. For example, the implementation unit implements a CO2 reduction plan. The system can also implement forest conservation activities. For example, the implementation unit can implement improving energy efficiency. The implementation unit can also implement reforestation activities. The evaluation unit evaluates the effectiveness of the countermeasures implemented by the implementation unit. For example, the evaluation unit records user behavior and evaluates the effectiveness. For example, the evaluation unit collects behavior logs and evaluates the effectiveness.Furthermore, the effectiveness can be evaluated using evaluation indicators. This allows the environmental sharing and improvement system according to the embodiment to grasp the current state of the global environment, predict future changes, propose and implement specific countermeasures, and evaluate their effectiveness.

[0068] The collection unit can collect data on temperature, precipitation, CO2 concentration, and deforestation rate. The collection unit, for example, collects temperature data using a temperature sensor. For example, the collection unit installs a temperature sensor and periodically collects temperature data. The collection unit can also collect precipitation data using a precipitation sensor. For example, the collection unit installs a precipitation sensor and collects precipitation data. The collection unit can also collect CO2 concentration data using a CO2 sensor. For example, the collection unit installs a CO2 sensor and collects CO2 concentration data. The collection unit can also analyze satellite images to measure the deforestation rate. For example, the collection unit acquires satellite images and calculates the deforestation rate using image analysis technology. This allows the collection unit to collect a variety of data related to the global environment.

[0069] The analysis unit analyzes the collected data and can grasp the current state of the global environment. The analysis unit analyzes the data using, for example, statistical analysis. For example, the analysis unit calculates the average value of temperature data. The analysis unit can also analyze the data using a machine learning algorithm. For example, the analysis unit analyzes fluctuations in CO2 concentration data. The analysis unit can also analyze correlations in the data. For example, the analysis unit analyzes the correlation between temperature and precipitation. This allows the analysis unit to accurately grasp the current state of the global environment.

[0070] The prediction unit can predict future changes in the global environment by comparing it with past data. For example, the prediction unit can predict future temperature changes by comparing them with past data. For example, the prediction unit can predict future temperature increases based on temperature data from the past 10 years. The prediction unit can also predict future changes in precipitation using simulation models. For example, the prediction unit can predict future changes in precipitation using simulation models. The prediction unit can also predict increases in CO2 concentration. For example, the prediction unit can predict future increases in CO2 concentration based on past CO2 concentration data. In this way, the prediction unit can predict future changes in the global environment.

[0071] Based on the predicted results, the proposal committee can propose specific measures to reduce CO2 emissions, promote forest conservation activities, and encourage the use of renewable energy. For example, the proposal committee can propose methods to reduce CO2 emissions. For example, the proposal committee can propose improving energy efficiency. The proposal committee can also propose the use of renewable energy. For example, the proposal committee can propose the introduction of solar power generation. The proposal committee can also propose promoting forest conservation activities. For example, the proposal committee can propose promoting afforestation activities. The proposal committee can also propose preventing illegal logging. For example, the proposal committee can propose monitoring for illegal logging. This allows the proposal committee to propose specific countermeasures.

[0072] The implementing body can carry out the proposed countermeasures. For example, the implementing body can implement a CO2 reduction plan. For example, the implementing body can implement energy efficiency improvements. The implementing body can also implement the use of renewable energy. For example, the implementing body can implement the introduction of solar power generation. The implementing body can also carry out forest conservation activities. For example, the implementing body can carry out afforestation activities. The implementing body can also carry out measures to prevent illegal logging. For example, the implementing body can monitor illegal logging. In this way, the implementing body can carry out the proposed countermeasures.

[0073] The evaluation unit can record the user's actions and evaluate the effects. The evaluation unit, for example, records the user's actions. For example, the evaluation unit collects action logs. The evaluation unit can also evaluate the effects. For example, the evaluation unit evaluates the effects using evaluation indices. For example, the evaluation unit evaluates the effects of reducing CO2 emissions. The evaluation unit can also evaluate the effects of forest protection activities. For example, the evaluation unit evaluates the effects of reforestation activities. This allows the evaluation unit to record the user's actions and evaluate their effects.

[0074] The shared environment improvement system further includes a collection unit that estimates a user's emotions and adjusts the timing of data collection based on the estimated user emotions. For example, if the user is feeling stressed, the collection unit reduces the frequency of data collection to reduce the burden on the user. For example, if the collection unit estimates the user's emotions and determines that the user is feeling stressed, it reduces the frequency of data collection. The collection unit can also increase the frequency of data collection and collect more detailed data if the user is relaxed. For example, if the collection unit estimates the user's emotions and determines that the user is relaxed, it increases the frequency of data collection. The collection unit can also adjust the timing of data collection to quickly collect necessary data if the user is in a hurry. For example, if the collection unit estimates the user's emotions and determines that the user is in a hurry, it adjusts the timing of data collection. This allows the collection unit to adjust the timing of data collection according to the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit may input the user's facial expression data into the generation AI and cause the generation AI to estimate the emotion.

[0075] The collection unit can analyze past data collection history and select the optimal collection method. The collection unit, for example, selects the most efficient collection method from the past data collection history. For example, the collection unit analyzes the past data collection history and selects the most efficient collection method. The collection unit can also optimize the collection frequency based on the past data collection history. For example, the collection unit analyzes the past data collection history and optimizes the collection frequency. The collection unit can also analyze the past data collection history and determine the priority of collection targets. For example, the collection unit analyzes the past data collection history and determines the priority of collection targets. This allows the collection unit to select the optimal collection method based on the past data collection history.

[0076] The collection unit can filter data based on specific environmental conditions or events when collecting data. For example, the collection unit collects data only when the temperature is above a certain level. For example, the collection unit collects data only when the temperature is above a certain level. The collection unit can also collect data when a specific event (e.g., a forest fire) occurs. For example, the collection unit collects data when a forest fire occurs. The collection unit can also collect data only when the CO2 concentration is above a certain level. For example, the collection unit collects data only when the CO2 concentration is above a certain level. This allows the collection unit to filter data based on specific environmental conditions or events.

[0077] The collection unit can estimate the user's emotions and determine the priority of data to be collected based on the estimated user emotions. For example, if the user is feeling stressed, the collection unit prioritizes collecting data of high importance. For example, if the collection unit estimates the user's emotions and determines that the user is feeling stressed, it prioritizes collecting data of high importance. The collection unit can also prioritize collecting detailed data if the user is relaxed. For example, if the collection unit estimates the user's emotions and determines that the user is relaxed, it prioritizes collecting detailed data. The collection unit can also prioritize collecting data that can be collected quickly if the user is in a hurry. For example, if the collection unit estimates the user's emotions and determines that the user is in a hurry, it prioritizes collecting data that can be collected quickly. This allows the collection unit to prioritize the data to be collected according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit may input the user's facial expression data into the generation AI and cause the generation AI to estimate the emotion.

[0078] When collecting data, the collection unit can prioritize collecting highly relevant data by taking geographical location information into consideration. The collection unit, for example, prioritizes collecting temperature data from a specific region. For example, the collection unit prioritizes collecting temperature data from a specific region by taking geographical location information into consideration. The collection unit can also prioritize collecting data from regions with high CO2 concentrations based on the geographical location information. For example, the collection unit prioritizes collecting data from regions with high CO2 concentrations by taking geographical location information into consideration. The collection unit can also prioritize collecting data from regions with high deforestation rates by taking geographical location information into consideration. For example, the collection unit prioritizes collecting data from regions with high deforestation rates by taking geographical location information into consideration. This allows the collection unit to prioritize collecting highly relevant data based on the geographical location information.

[0079] The collection unit can analyze social media activities and collect related data when collecting data. For example, the collection unit collects data on environmental issues that are trending on social media. For example, the collection unit collects data on environmental issues that are trending on social media. The collection unit can also analyze posts on social media and collect environmental data for a specific region. For example, the collection unit analyzes posts on social media and collects environmental data for a specific region. The collection unit can also collect related environmental data based on trends on social media. For example, the collection unit collects related environmental data based on trends on social media. In this way, the collection unit can collect related data based on social media activities.

[0080] The analysis unit can estimate the user's emotions and adjust the way the analysis is presented based on the estimated user's emotions. For example, if the user is nervous, the analysis unit provides a simple, highly visible analysis result. For example, if the analysis unit estimates the user's emotions and determines that the user is nervous, it provides a simple, highly visible analysis result. The analysis unit can also provide a detailed analysis result if the user is relaxed. For example, if the analysis unit estimates the user's emotions and determines that the user is relaxed, it provides a detailed analysis result. The analysis unit can also provide a summary analysis result if the user is in a hurry. For example, if the analysis unit estimates the user's emotions and determines that the user is in a hurry, it provides a summary analysis result. This allows the analysis unit to adjust the way the analysis is presented based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be, 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, for example, AI, or may be performed without using AI. For example, the analysis unit may input the user's facial expression data into the generation AI and have the generation AI estimate the emotion.

[0081] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the data. The analysis unit, for example, performs a detailed analysis on data with high importance. For example, the analysis unit evaluates the importance of the data and performs a detailed analysis on the data with high importance. The analysis unit can also perform a simplified analysis on data with low importance. For example, the analysis unit evaluates the importance of the data and performs a simplified analysis on the data with low importance. The analysis unit can also determine the priority of the analysis based on the importance of the data. For example, the analysis unit evaluates the importance of the data and determines the priority of the analysis. This allows the analysis unit to adjust the level of detail of the analysis based on the importance of the data.

[0082] During analysis, the analysis unit can apply different analysis algorithms depending on the category of data. For example, the analysis unit applies a specific analysis algorithm to temperature data. For example, the analysis unit applies a specific analysis algorithm to temperature data. The analysis unit can also apply a different analysis algorithm to CO2 concentration data. For example, the analysis unit applies a different analysis algorithm to CO2 concentration data. The analysis unit can also apply a dedicated analysis algorithm to deforestation rate data. For example, the analysis unit applies a dedicated analysis algorithm to deforestation rate data. This allows the analysis unit to apply an appropriate analysis algorithm depending on the category of data.

[0083] The analysis unit can estimate the user's emotions and adjust the length of the analysis based on the estimated user emotions. For example, if the user is in a hurry, the analysis unit provides a short and concise analysis result. For example, if the analysis unit estimates the user's emotions and determines that the user is in a hurry, it provides a short and concise analysis result. The analysis unit can also provide a detailed analysis result if the user is relaxed. For example, if the analysis unit estimates the user's emotions and determines that the user is relaxed, it provides a detailed analysis result. The analysis unit can also provide a visually stimulating analysis result if the user is excited. For example, if the analysis unit estimates the user's emotions and determines that the user is excited, it provides a visually stimulating analysis result. This allows the analysis unit to adjust the length of the analysis according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be, 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, for example, AI, or may be performed without using AI. For example, the analysis unit may input the user's facial expression data into the generation AI and have the generation AI estimate the emotion.

[0084] During analysis, the analysis unit can determine the order of analysis priorities based on the time when the data was collected. The analysis unit, for example, prioritizes analyzing the most recent data. For example, the analysis unit prioritizes analyzing the most recent data, taking into account the time when the data was collected. The analysis unit can also analyze trends based on past data. For example, the analysis unit analyzes trends based on past data. The analysis unit can also determine the order of analysis based on the time when the data was collected. For example, the analysis unit decides the order of analysis, taking into account the time when the data was collected. This allows the analysis unit to determine the order of analysis priorities based on the time when the data was collected.

[0085] During analysis, the analysis unit can adjust the order of analysis based on the relevance of the data. The analysis unit, for example, prioritizes analysis of highly relevant data. For example, the analysis unit evaluates the relevance of the data and prioritizes analysis of highly relevant data. The analysis unit can also postpone data with low relevance. For example, the analysis unit evaluates the relevance of the data and postpones analysis of data with low relevance. The analysis unit can also optimize the order of analysis based on the relevance of the data. For example, the analysis unit evaluates the relevance of the data and optimizes the order of analysis. This allows the analysis unit to adjust the order of analysis based on the relevance of the data.

[0086] The prediction unit can estimate the user's emotions and adjust prediction criteria based on the estimated user emotions. For example, if the user is nervous, the prediction unit provides a simple and highly visible prediction result. For example, if the prediction unit estimates the user's emotions and determines that the user is nervous, the prediction unit provides a simple and highly visible prediction result. The prediction unit can also provide a detailed prediction result if the user is relaxed. For example, if the prediction unit estimates the user's emotions and determines that the user is relaxed, the prediction unit provides a detailed prediction result. The prediction unit can also provide a prediction result that focuses on the main points if the user is in a hurry. For example, if the prediction unit estimates the user's emotions and determines that the user is in a hurry, the prediction unit provides a prediction result that focuses on the main points. This allows the prediction unit to adjust prediction criteria according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be, 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 prediction unit may be performed using, for example, AI, or may be performed without using AI. For example, the prediction unit may input the user's facial expression data into the generation AI and cause the generation AI to estimate the emotion.

[0087] The prediction unit can improve the accuracy of the prediction by taking into account the interrelationship between data when making a prediction. The prediction unit, for example, makes a prediction by taking into account the interrelationship between temperature and precipitation. For example, the prediction unit makes a prediction by taking into account the interrelationship between temperature and precipitation. The prediction unit can also make a prediction by taking into account the interrelationship between CO2 concentration and deforestation rate. For example, the prediction unit makes a prediction by taking into account the interrelationship between CO2 concentration and deforestation rate. The prediction unit can also improve the accuracy of the prediction based on the interrelationship between data. For example, the prediction unit improves the accuracy of the prediction based on the interrelationship between data. In this way, the prediction unit can improve the accuracy of the prediction by taking into account the interrelationship between data.

[0088] When making a prediction, the prediction unit can make a prediction taking into account attribute information of the data submitter. The prediction unit, for example, makes a prediction taking into account the specialized knowledge of the data submitter. For example, the prediction unit makes a prediction taking into account the specialized knowledge of the data submitter. The prediction unit can also make a prediction based on the past performance of the data submitter. For example, the prediction unit makes a prediction based on the past performance of the data submitter. The prediction unit can also improve the accuracy of the prediction based on the attribute information of the data submitter. For example, the prediction unit improves the accuracy of the prediction based on the attribute information of the data submitter. This allows the prediction unit to improve the accuracy of the prediction taking into account the attribute information of the data submitter.

[0089] The prediction unit can estimate the user's emotions and adjust the order in which prediction results are displayed based on the estimated user emotions. For example, if the user is nervous, the prediction unit displays important prediction results first. For example, if the prediction unit estimates the user's emotions and determines that the user is nervous, it displays important prediction results first. The prediction unit can also sequentially display detailed prediction results if the user is relaxed. For example, if the prediction unit estimates the user's emotions and determines that the user is relaxed, it displays detailed prediction results sequentially. The prediction unit can also display prediction results that emphasize the main points first if the user is in a hurry. For example, if the prediction unit estimates the user's emotions and determines that the user is in a hurry, it displays prediction results that emphasize the main points first. This allows the prediction unit to adjust the order in which prediction results are displayed depending 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 prediction unit may be performed using, for example, AI, or may be performed without using AI. For example, the prediction unit may input the user's facial expression data into the generation AI and cause the generation AI to estimate the emotion.

[0090] The prediction unit can make predictions taking into account the geographical distribution of data when making predictions. The prediction unit, for example, makes predictions based on data from geographically close areas. For example, the prediction unit makes predictions based on data from geographically close areas. The prediction unit can also make predictions based on data from geographically distant areas. For example, the prediction unit makes predictions based on data from geographically distant areas. The prediction unit can also improve the accuracy of predictions by taking into account the geographical distribution. For example, the prediction unit improves the accuracy of predictions by taking into account the geographical distribution. This allows the prediction unit to improve the accuracy of predictions by taking into account the geographical distribution of data.

[0091] The prediction unit can improve the accuracy of prediction by referring to related literature during prediction. The prediction unit, for example, improves the accuracy of prediction based on related literature. For example, the prediction unit improves the accuracy of prediction based on related literature. The prediction unit can also adjust the prediction criteria by referring to related literature. For example, the prediction unit adjusts the prediction criteria by referring to related literature. The prediction unit can also complement the prediction result based on related literature. For example, the prediction unit complements the prediction result based on related literature. In this way, the prediction unit can improve the accuracy of prediction by referring to related literature.

[0092] The suggestion unit can estimate the user's emotions and adjust the way it presents its suggestions based on those emotions. For example, if the user is nervous, the suggestion unit can provide simple and easy-to-understand suggestions. For example, if the suggestion unit estimates the user's emotions and determines that they are nervous, it can provide simple and easy-to-understand suggestions. The suggestion unit can also provide detailed suggestions if the user is relaxed. For example, if the suggestion unit estimates the user's emotions and determines that they are relaxed, it can provide detailed suggestions. The suggestion unit can also provide concise suggestions if the user is in a hurry. For example, if the suggestion unit estimates the user's emotions and determines that they are in a hurry, it can provide concise suggestions. In this way, the suggestion unit can adjust the way it presents its suggestions according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input the user's facial expression data into the generation AI and have the generation AI estimate the emotion.

[0093] The proposal unit can adjust the level of detail of the proposal based on the importance of the countermeasure when making a proposal. For example, the proposal unit makes a detailed proposal for a countermeasure with high importance. For example, the proposal unit evaluates the importance of the countermeasure and makes a detailed proposal for the countermeasure with high importance. The proposal unit can also make a simplified proposal for a countermeasure with low importance. For example, the proposal unit evaluates the importance of the countermeasure and makes a simplified proposal for the countermeasure with low importance. The proposal unit can also determine the priority of the proposal based on the importance of the countermeasure. For example, the proposal unit evaluates the importance of the countermeasure and determines the priority of the proposal. This allows the proposal unit to adjust the level of detail of the proposal based on the importance of the countermeasure.

[0094] When making a proposal, the proposal unit can apply different proposal algorithms depending on the category of the countermeasure. For example, the proposal unit applies a specific algorithm to proposals for reducing CO2 emissions. For example, the proposal unit applies a specific algorithm to proposals for reducing CO2 emissions. The proposal unit can also apply a different algorithm to proposals for forest protection activities. For example, the proposal unit applies a different algorithm to proposals for forest protection activities. The proposal unit can also apply a dedicated algorithm to proposals for promoting the use of renewable energy. For example, the proposal unit applies a dedicated algorithm to proposals for promoting the use of renewable energy. This allows the proposal unit to apply an appropriate proposal algorithm depending on the category of the countermeasure.

[0095] The suggestion unit can estimate the user's emotions and adjust the length of the suggestions based on the estimated user's emotions. For example, if the user is in a hurry, the suggestion unit provides short and to-the-point suggestions. For example, if the suggestion unit estimates the user's emotions and determines that the user is in a hurry, it provides short and to-the-point suggestions. The suggestion unit can also provide detailed suggestions if the user is relaxed. For example, if the suggestion unit estimates the user's emotions and determines that the user is relaxed, it provides detailed suggestions. The suggestion unit can also provide visually stimulating suggestions if the user is excited. For example, if the suggestion unit estimates the user's emotions and determines that the user is excited, it provides visually stimulating suggestions. This allows the suggestion unit to adjust the length of the suggestions according to the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the suggestion unit can be performed, for example, using AI or without AI. For example, the suggestion unit can input the user's facial expression data into the generation AI and have the generation AI estimate the emotion.

[0096] When making a proposal, the proposal unit can determine the priority of the proposals based on the time when the countermeasures were submitted. The proposal unit, for example, gives priority to proposing the most recent countermeasure. For example, the proposal unit gives priority to proposing the most recent countermeasure, taking into account the time when the countermeasures were submitted. The proposal unit can also propose trends based on past countermeasures. For example, the proposal unit can propose trends based on past countermeasures. The proposal unit can also determine the order of the proposals based on the time when the proposals were submitted. For example, the proposal unit determines the order of the proposals, taking into account the time when the countermeasures were submitted. This allows the proposal unit to determine the priority of the proposals based on the time when the countermeasures were submitted.

[0097] The proposal unit can adjust the order of proposals based on the relevance of the countermeasures when making proposals. The proposal unit, for example, prioritizes proposing highly relevant countermeasures. For example, the proposal unit evaluates the relevance of the countermeasures and prioritizes proposing highly relevant countermeasures. The proposal unit can also postpone less relevant countermeasures. For example, the proposal unit evaluates the relevance of the countermeasures and postpones less relevant countermeasures. The proposal unit can also optimize the order of proposals based on the relevance of the countermeasures. For example, the proposal unit evaluates the relevance of the countermeasures and optimizes the order of proposals. This allows the proposal unit to adjust the order of proposals based on the relevance of the countermeasures.

[0098] The execution unit can estimate the user's emotions and adjust the execution method based on the estimated user's emotions. For example, if the user is nervous, the execution unit provides a simple and highly visible execution method. For example, if the execution unit estimates the user's emotions and determines that the user is nervous, it provides a simple and highly visible execution method. The execution unit can also provide a detailed execution method if the user is relaxed. For example, if the execution unit estimates the user's emotions and determines that the user is relaxed, it provides a detailed execution method. The execution unit can also provide a more concise execution method if the user is in a hurry. For example, if the execution unit estimates the user's emotions and determines that the user is in a hurry, it provides a more concise execution method. This allows the execution unit to adjust the execution method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be, 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 execution unit may be performed using, for example, AI, or may be performed without using AI. For example, the execution unit may input the user's facial expression data into the generation AI and have the generation AI estimate the emotion.

[0099] At the time of execution, the execution unit can analyze past execution history and select the optimal execution method. The execution unit, for example, selects the most efficient execution method from the past execution history. For example, the execution unit analyzes the past execution history and selects the most efficient execution method. The execution unit can also optimize the execution frequency based on the past execution history. For example, the execution unit analyzes the past execution history and optimizes the execution frequency. The execution unit can also analyze the past execution history and determine the priority of execution targets. For example, the execution unit analyzes the past execution history and determine the priority of execution targets. This allows the execution unit to select the optimal execution method based on the past execution history.

[0100] At the time of execution, the execution unit can customize the means of execution based on the current environmental conditions. The execution unit selects the optimal means of execution based on, for example, the current temperature. For example, the execution unit selects the optimal means of execution based on the current temperature. The execution unit can also customize the means of execution based on the current CO2 concentration. For example, the execution unit customizes the means of execution based on the current CO2 concentration. The execution unit can also adjust the means of execution based on the current rate of deforestation. For example, the execution unit adjusts the means of execution based on the current rate of deforestation. In this way, the execution unit can customize the means of execution based on the current environmental conditions.

[0101] The execution unit can estimate the user's emotions and determine the priority of execution based on the estimated user's emotions. For example, if the user is feeling stressed, the execution unit prioritizes execution with a higher priority. For example, if the execution unit estimates the user's emotions and determines that the user is feeling stressed, the execution unit prioritizes execution with a higher priority. The execution unit can also prioritize detailed execution if the user is relaxed. For example, if the execution unit estimates the user's emotions and determines that the user is relaxed, the execution unit prioritizes detailed execution. The execution unit can also prioritize measures that can be executed quickly if the user is in a hurry. For example, if the execution unit estimates the user's emotions and determines that the user is in a hurry, the execution unit prioritizes measures that can be executed quickly. This allows the execution unit to determine the priority of execution according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the execution unit may be performed using, for example, AI, or may be performed without using AI. For example, the execution unit may input the user's facial expression data into the generation AI and have the generation AI estimate the emotion.

[0102] The execution unit can select the optimal execution method at the time of execution by taking geographical location information into consideration. The execution unit, for example, selects the execution method based on data of a geographically close area. For example, the execution unit selects the execution method based on data of a geographically close area. The execution unit can also select the execution method based on data of a geographically distant area. For example, the execution unit selects the execution method based on data of a geographically distant area. The execution unit can also select the optimal execution method by taking geographical location information into consideration. For example, the execution unit selects the optimal execution method by taking geographical location information into consideration. This allows the execution unit to select the optimal execution method based on the geographical location information.

[0103] During execution, the execution unit can analyze social media activity and suggest action measures. For example, the execution unit suggests action measures related to environmental issues that are trending on social media. For example, the execution unit suggests action measures related to environmental issues that are trending on social media. The execution unit can also analyze posts on social media and suggest action measures for a specific region. For example, the execution unit can analyze posts on social media and suggest action measures for a specific region. The execution unit can also suggest related action measures based on trends on social media. For example, the execution unit suggests related action measures based on trends on social media. In this way, the execution unit can suggest related action measures based on social media activity.

[0104] The evaluation unit can estimate the user's emotions and adjust the evaluation method based on the estimated emotions. For example, if the user is nervous, the evaluation unit can provide a simple and easy-to-understand evaluation method. For example, if the evaluation unit estimates the user's emotions and determines that they are nervous, it can provide a simple and easy-to-understand evaluation method. The evaluation unit can also provide a detailed evaluation method if the user is relaxed. For example, if the evaluation unit estimates the user's emotions and determines that they are relaxed, it can provide a detailed evaluation method. The evaluation unit can also provide a concise evaluation method if the user is in a hurry. For example, if the evaluation unit estimates the user's emotions and determines that they are in a hurry, it can provide a concise evaluation method. In this way, the evaluation unit can adjust the evaluation method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is 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 evaluation unit may be performed using, for example, AI, or may be performed without using AI. For example, the evaluation unit may input the user's facial expression data into the generation AI and have the generation AI estimate the emotion.

[0105] The evaluation unit can optimize the evaluation algorithm by referring to past evaluation data during evaluation. The evaluation unit, for example, optimizes the evaluation algorithm based on past evaluation data. For example, the evaluation unit optimizes the evaluation algorithm based on past evaluation data. The evaluation unit can also adjust the evaluation criteria by referring to past evaluation data. For example, the evaluation unit adjusts the evaluation criteria by referring to past evaluation data. The evaluation unit can also improve the accuracy of the evaluation based on the past evaluation data. For example, the evaluation unit improves the accuracy of the evaluation based on the past evaluation data. This allows the evaluation unit to optimize the evaluation algorithm based on the past evaluation data.

[0106] The evaluation unit can estimate the user's emotions and adjust the frequency of evaluations based on the estimated user's emotions. For example, the evaluation unit reduces the frequency of evaluations when the user is feeling stressed. For example, the evaluation unit estimates the user's emotions and determines that the user is feeling stressed, and reduces the frequency of evaluations. The evaluation unit can also increase the frequency of evaluations when the user is relaxed. For example, the evaluation unit estimates the user's emotions and determines that the user is relaxed, and increases the frequency of evaluations. The evaluation unit can also adjust the timing of evaluations when the user is in a hurry. For example, the evaluation unit estimates the user's emotions and determines that the user is in a hurry, and adjusts the timing of evaluations. This allows the evaluation unit to adjust the frequency of evaluations according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be 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, for example, an AI, or may be performed without using an AI. For example, the evaluation unit can input the user's facial expression data into the generation AI and have the generation AI estimate the emotion.

[0107] During evaluation, the evaluation unit can weight the evaluation data based on the time when the data was collected. The evaluation unit, for example, weights the most recent data highly. For example, the evaluation unit takes into account the time when the data was collected and weights the most recent data highly. The evaluation unit can also weight older data lightly. For example, the evaluation unit takes into account the time when the data was collected and weights the older data lightly. The evaluation unit can also adjust the weighting of the evaluation data based on the time when the data was collected. For example, the evaluation unit adjusts the weighting of the evaluation data based on the time when the data was collected. This allows the evaluation unit to weight the evaluation data based on the time when the data was collected. === Hard Collateral 1-1 === Each of the multiple elements described above, including the data collection unit, analysis unit, prediction unit, proposal unit, execution unit, and evaluation unit, is implemented, for example, in at least one of the smart device 14 and the data processing unit 12. For example, the data collection unit collects data on the global environment using the camera 42 and sensors of the smart device 14 and analyzes it using the specific processing unit 290 of the data processing unit 12. The analysis unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and analyzes the collected data. The prediction unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and predicts changes in the global environment based on the analyzed data. The proposal unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and proposes specific countermeasures based on the predicted results. The execution unit is implemented, for example, by the control unit 46A of the smart device 14 and executes the proposed countermeasures. The evaluation unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and evaluates the effectiveness of the executed countermeasures. Furthermore, the collection unit has a function of estimating the user's emotion and adjusting the timing of data collection based on the estimated emotion. The emotion estimation is realized by, for example, the specific processing unit 290 of the data processing device 12. === Hard Collateral 1-2 === Each of the multiple elements, including the collection unit, analysis unit, prediction unit, proposal unit, execution 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 collects data related to the global environment using the camera 42 or a sensor of the smart glasses 214, and the data is analyzed by the specific processing unit 290 of the data processing device 12. The analysis unit, realized, for example, by the specific processing unit 290 of the data processing device 12, analyzes the collected data. The prediction unit, realized, for example, by the specific processing unit 290 of the data processing device 12, predicts changes in the global environment based on the analyzed data. The proposal unit, realized, for example, by the specific processing unit 290 of the data processing device 12, proposes specific countermeasures based on the predicted results. The execution unit, realized, for example, by the control unit 46A of the smart glasses 214, executes the proposed countermeasures. The evaluation unit, realized, for example, by the specific processing unit 290 of the data processing device 12, evaluates the effectiveness of the implemented countermeasures. Furthermore, the collection unit has a function of estimating the user's emotion and adjusting the timing of data collection based on the estimated emotion. The emotion estimation is realized by, for example, the specific processing unit 290 of the data processing device 12. === Hard Collateral 1-3 === Each of the multiple elements, including the collection unit, analysis unit, prediction unit, proposal unit, execution unit, and evaluation unit, 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 collects data related to the global environment using the camera 42 or sensors of the headset-type terminal 314, and the data is analyzed by the specific processing unit 290 of the data processing device 12. The analysis unit, realized, for example, by the specific processing unit 290 of the data processing device 12, analyzes the collected data. The prediction unit, realized, for example, by the specific processing unit 290 of the data processing device 12, predicts changes in the global environment based on the analyzed data. The proposal unit, realized, for example, by the specific processing unit 290 of the data processing device 12, proposes specific countermeasures based on the predicted results. The execution unit, realized, for example, by the control unit 46A of the headset-type terminal 314, executes the proposed countermeasures. The evaluation unit, realized, for example, by the specific processing unit 290 of the data processing device 12, evaluates the effectiveness of the implemented countermeasures. Furthermore, the collection unit has a function of estimating the user's emotion and adjusting the timing of data collection based on the estimated emotion. The emotion estimation is realized by, for example, the specific processing unit 290 of the data processing device 12. === Hard Collateral 1-4 === Each of the multiple elements, including the collection unit, analysis unit, prediction unit, proposal unit, execution unit, and evaluation unit, is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the collection unit collects data related to the global environment using the camera 42 and sensors of the robot 414, and the data is analyzed by the specific processing unit 290 of the data processing device 12. The analysis unit, realized, for example, by the specific processing unit 290 of the data processing device 12, analyzes the collected data. The prediction unit, realized, for example, by the specific processing unit 290 of the data processing device 12, predicts changes in the global environment based on the analyzed data. The proposal unit, realized, for example, by the specific processing unit 290 of the data processing device 12, proposes specific countermeasures based on the predicted results. The execution unit, realized, for example, by the control unit 46A of the robot 414, executes the proposed countermeasures. The evaluation unit, realized, for example, by the specific processing unit 290 of the data processing device 12, evaluates the effectiveness of the implemented countermeasures. Furthermore, the collection unit has a function of estimating the user's emotion and adjusting the timing of data collection based on the estimated emotion. The emotion estimation is realized by, for example, the specific processing unit 290 of the data processing device 12.

[0108] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0109] When analyzing data, the analysis unit can evaluate the reliability of the data and prioritize analysis of highly reliable data. For example, the analysis unit evaluates the source and collection method of the data and prioritizes analysis of highly reliable data. The analysis unit can also adjust the accuracy of the analysis results based on the reliability of the data. For example, the accuracy of the analysis results is set low for data with low reliability. Furthermore, the analysis unit can determine the priority of analysis based on the reliability of the data. This allows the analysis unit to adjust the accuracy and priority of analysis taking into account the reliability of the data.

[0110] The collection unit can analyze the user's behavioral patterns when collecting data and determine the optimal collection timing. For example, the collection unit collects data during times when the user is active. The collection unit can also refrain from collecting data during times when the user is resting. Furthermore, the collection unit can adjust the frequency of data collection based on the user's behavioral patterns. This allows the collection unit to determine the optimal data collection timing taking the user's behavioral patterns into consideration.

[0111] The prediction unit can detect outliers during prediction and remove the outliers to improve the accuracy of the prediction. For example, the prediction unit can detect and remove outliers in a dataset. The prediction unit can also correct the outliers to minimize the impact of the outliers. Furthermore, the prediction unit can adjust the prediction model based on the outlier detection result. This allows the prediction unit to improve the accuracy of the prediction by taking the outliers into account.

[0112] When making a suggestion, the suggestion unit can refer to the user's past behavioral history and make the most suitable suggestion to the user. For example, the suggestion unit analyzes the user's past behavioral history and makes a suggestion that is easy for the user to implement. The suggestion unit can also customize the content of the suggestion based on the user's past behavioral history. Furthermore, the suggestion unit can determine the priority of the suggestion taking into account the user's past behavioral history. This allows the suggestion unit to make the most suitable suggestion taking into account the user's past behavioral history.

[0113] The execution unit can collect user feedback during execution and improve its execution method. For example, the execution unit collects user feedback and improves its execution method. It can also adjust the execution procedure based on user feedback. Furthermore, the execution unit can determine execution priorities considering user feedback. This allows the execution unit to improve its execution method by reflecting user feedback.

[0114] The evaluation unit can estimate the user's emotions and adjust the evaluation criteria based on the estimated user's emotions. For example, the evaluation unit can relax the evaluation criteria when the user is feeling stressed. The evaluation unit can also tighten the evaluation criteria when the user is relaxed. Furthermore, the evaluation unit can adjust the frequency of evaluation based on the user's emotions. This allows the evaluation unit to adjust the evaluation criteria and frequency in consideration of the user's emotions.

[0115] The collection unit can estimate the user's emotions and adjust the data collection method based on the estimated user's emotions. For example, the collection unit can simplify the data collection method when the user is feeling stressed. Alternatively, the collection unit can perform detailed data collection when the user is relaxed. Furthermore, the collection unit can adjust the timing of data collection based on the user's emotions. This allows the collection unit to adjust the method and timing of data collection taking the user's emotions into consideration.

[0116] The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated user's emotions. For example, if the user is nervous, the analysis unit can provide simple, highly visible analysis results. Alternatively, if the user is relaxed, the analysis unit can provide detailed analysis results. Furthermore, the analysis unit can adjust the display order of the analysis results based on the user's emotions. This allows the analysis unit to adjust the display method of the analysis results taking the user's emotions into consideration.

[0117] The prediction unit can estimate the user's emotion and adjust the display method of the prediction result based on the estimated user's emotion. For example, if the user is nervous, the prediction unit can provide a simple and highly visible prediction result. Alternatively, if the user is relaxed, the prediction unit can provide a detailed prediction result. Furthermore, the prediction unit can adjust the display order of the prediction results based on the user's emotion. This allows the prediction unit to adjust the display method of the prediction result taking the user's emotion into consideration.

[0118] The suggestion unit can estimate the user's emotions and adjust the content of suggestions based on the estimated user's emotions. For example, when the user is feeling stressed, the suggestion unit can provide simple and easy-to-implement suggestions. When the user is relaxed, the suggestion unit can also provide detailed and specific suggestions. Furthermore, the suggestion unit can adjust the priority of suggestions based on the user's emotions. This allows the suggestion unit to adjust the content and priority of suggestions taking the user's emotions into consideration.

[0119] The processing flow of the second embodiment will be briefly explained below.

[0120] Step 1: The collection unit collects data related to the global environment. For example, data on temperature, precipitation, CO2 concentration, deforestation rate, etc. The collection unit collects this data using temperature sensors, precipitation sensors, and CO2 sensors. Step 2: The analysis unit analyzes the data collected by the collection unit. The analysis unit analyzes the data using statistical analysis and machine learning algorithms. For example, the analysis unit calculates the average value of the temperature data and analyzes fluctuations in the CO2 concentration data. Step 3: The forecasting unit predicts changes in the global environment based on the data analyzed by the analysis unit. The forecasting unit compares data from the past to predict future changes in temperature, and uses simulation models to predict future changes in precipitation. Step 4: The Proposal Department proposes specific countermeasures based on the results predicted by the Forecasting Department, such as ways to reduce CO2 emissions and promote forest conservation activities. Step 5: The Implementation Department implements the measures proposed by the Proposal Department. The Implementation Department implements CO2 reduction plans and forest protection activities. Step 6: The evaluation unit evaluates the effectiveness of the countermeasures implemented by the execution unit. The evaluation unit collects user action logs and evaluates the effectiveness using evaluation indices.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0125] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0126] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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).

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0141] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0142] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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).

[0147] 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.

[0148] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset 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.

[0149] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0150] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0151] In the 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.

[0152] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0153] The specific processing unit 290 transmits the result of the specific processing to the 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.

[0154] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt 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.

[0155] 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.

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

[0157] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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).

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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).

[0178] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0179] 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."

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] 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.

[0189] 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.

[0190] 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.

[0191] 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.

[0192] [Explanation of symbols]

[0193] 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 related to the global environment; an analysis unit that analyzes the data collected by the collection unit; a prediction unit that predicts changes in the global environment based on the data analyzed by the analysis unit; a proposal unit that proposes specific countermeasures based on the results predicted by the prediction unit; an execution unit that executes the countermeasure proposed by the proposal unit; an evaluation unit that evaluates the effectiveness of the countermeasures executed by the execution unit; Equipped with A system characterized by:

2. The collecting unit Collect data on temperature, precipitation, CO2 concentration, and deforestation rates The system of claim 1 .

3. The analysis unit Analyzing the collected data to understand the current state of the global environment The system of claim 1 .

4. The prediction unit Comparing data from the past to predict future changes in the global environment The system of claim 1 .

5. The proposal unit Based on the predicted results, we will propose concrete measures to reduce CO2 emissions, promote forest conservation activities, and encourage the use of renewable energy. The system of claim 1 .

6. The execution unit: Implement the proposed measures The system of claim 1 .

7. The evaluation unit Record user behavior and evaluate its effectiveness. The system of claim 1 .

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

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