system

Abstract

The system according to the embodiment aims to centrally manage global problems and propose and implement solutions. [Solution] A system according to an embodiment includes a collection unit, an analysis unit, a proposal unit, and an execution unit. The collection unit collects data. The analysis unit analyzes the data collected by the collection unit. The proposal unit proposes a solution based on the analysis results obtained by the analysis unit. The execution unit executes the solution proposed by the proposal unit.

Description

[Technical Field]

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

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

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

[0004] Conventional technologies do not centrally manage data collection, analysis, proposals and implementation of solutions to global problems, and there is room for improvement.

[0005] The system according to the embodiment aims to centrally manage global problems and propose and implement solutions. [Means for solving the problem]

[0006] The system according to the embodiment includes a collection unit, an analysis unit, a proposal unit, and an execution unit. The collection unit collects data. The analysis unit analyzes the data collected by the collection unit. The proposal unit proposes a solution based on the analysis result obtained by the analysis unit. The execution unit executes the solution proposed by the proposal unit. [Effects of the Invention]

[0007] The system according to the embodiment can centrally manage global problems and propose and implement solutions. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) A program management system according to an embodiment of the present invention is a unified management system for global issues (weather, poverty, conflict, and environmental pollution). This program management system provides unified management from event prediction to resolution. Specifically, it utilizes big data to collect information on issues such as weather, poverty, conflict, and environmental pollution, and analyzes the collected data to predict the occurrence of events. For example, it can analyze weather data to predict the occurrence of abnormal weather. Next, it proposes solutions to the predicted events, provides and receives funding to implement the proposed solutions, and creates an implementation plan. For example, in the case of poverty, it proposes financial assistance and educational programs. Furthermore, it takes specific measures to solve the problems based on the implementation plan. For example, in the case of environmental pollution, it removes pollutants and introduces renewable energy. In this way, a unified management system for global issues is established, and problem solving is approached with strong information and response capabilities. As a result, the program management system realizes unified management of global issues and can approach problem solving with strong information and response capabilities.

[0029] A program management system according to an embodiment includes a collection unit, an analysis unit, a proposal unit, and an execution unit. The collection unit collects data. The collection unit collects information such as weather data and poverty data. For example, the collection unit acquires data such as temperature, precipitation, and wind speed to collect weather data. The collection unit can also acquire data such as income, living standards, and education levels to collect poverty data. The analysis unit analyzes the data collected by the collection unit. For example, the analysis unit analyzes the collected weather data and predicts the occurrence of abnormal weather. The analysis unit can analyze the data using, for example, statistical analysis or a machine learning algorithm. The proposal unit proposes solutions based on analysis results obtained by the analysis unit. For example, the proposal unit proposes financial assistance or educational programs to address poverty issues based on the analysis results. The proposal unit can propose, for example, technical or policy solutions. The execution unit implements the solutions proposed by the proposal unit. For example, the execution unit formulates plans for implementing the proposed solutions and takes specific measures. The execution unit can, for example, remove pollutants or introduce renewable energy. As a result, the program management system according to the embodiment can realize centralized management of global issues and can tackle problem solving with strong information and response capabilities.

[0030] The collection unit may collect weather data or poverty data. For example, to collect weather data, the collection unit acquires data such as temperature, precipitation, and wind speed. For example, the collection unit acquires data in real time from weather stations and stores it in a weather database. The collection unit may also acquire data such as income, living standards, and education levels to collect poverty data. For example, the collection unit collects statistical data provided by government agencies and non-governmental organizations and stores it in a poverty database. In this way, by collecting information such as weather data and poverty data, comprehensive data on global issues can be obtained.

[0031] The analysis unit can analyze the collected data and predict the occurrence of events. The analysis unit can, for example, analyze collected weather data and predict the occurrence of abnormal weather. For example, the analysis unit can analyze the data using statistical analysis or machine learning algorithms. For example, the analysis unit can calculate the probability of abnormal weather occurring based on the weather data and provide the prediction result. The analysis unit can also analyze the collected poverty data and predict the occurrence of poverty issues. For example, the analysis unit can calculate the probability of poverty occurring based on data on income and living standards and provide the prediction result. In this way, by analyzing the collected data and predicting the occurrence of events, it becomes possible to detect and respond to problems early.

[0032] The proposal unit can propose solutions based on the analysis results. For example, the proposal unit can propose financial assistance or educational programs to address poverty issues based on the analysis results. For example, the proposal unit can provide educational programs to poor areas to improve living standards. The proposal unit can also propose solutions to environmental pollution issues based on the analysis results. For example, the proposal unit can propose the removal of pollutants or the introduction of renewable energy. In this way, by proposing solutions based on the analysis results, appropriate countermeasures can be provided quickly.

[0033] The implementation unit can make a plan to implement the proposed solution and take specific measures. The implementation unit, for example, makes a plan to implement the proposed solution and takes specific measures. For example, the implementation unit can make a plan to remove pollutants and actually remove the pollutants. The implementation unit can also make a plan to introduce renewable energy and actually introduce the renewable energy. In this way, by making a plan to implement the proposed solution and taking specific measures, the ability to implement problem solving is improved.

[0034] The collection unit can analyze past data collection history and select an appropriate collection method. The collection unit, for example, selects the most efficient collection method from past data collection history. For example, the collection unit analyzes past data collection history and evaluates the efficiency of the collection method. The collection unit can also analyze past data collection history and identify areas for improvement in the collection method. For example, the collection unit optimizes the collection method based on past data collection history. In this way, by analyzing past data collection history, the optimal collection method can be selected, enabling efficient data collection.

[0035] The collection unit can perform filtering based on the current geographical situation and areas of interest when collecting data. The collection unit, for example, prioritizes collecting highly relevant data based on the current geographical situation. For example, the collection unit analyzes the current geographical situation and filters out highly relevant data. The collection unit can also filter the data to be collected based on the user's areas of interest. For example, the collection unit analyzes the user's areas of interest and prioritizes collecting highly relevant data. In this way, highly relevant data can be efficiently collected by filtering data based on the current geographical situation and areas of interest.

[0036] 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 information close to the current location. For example, the collection unit identifies the current location based on GPS data and prioritizes collecting highly relevant data. The collection unit can also prioritize collecting data that is highly geographically relevant. For example, the collection unit evaluates geographical relevance and prioritizes collecting highly relevant data. This enables efficient data collection by prioritizing the collection of highly relevant data by taking geographical location information into consideration.

[0037] The collection unit can analyze social media activity and collect relevant data when collecting data. The collection unit, for example, analyzes trends on social media and collects relevant data. For example, the collection unit analyzes the content of social media posts, identifies trends, and collects relevant data. The collection unit can also select data to collect based on the user's social media activity. For example, the collection unit analyzes the content of users' posts and reactions to collect highly relevant data. In this way, highly relevant data can be efficiently collected by analyzing social media activity.

[0038] 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. This allows for efficient analysis by adjusting the level of detail of the analysis based on the importance of the data.

[0039] During analysis, the analysis unit can apply different analysis algorithms depending on the category of data. For example, the analysis unit applies a weather forecasting algorithm to weather data. For example, the analysis unit uses a weather forecasting algorithm to analyze weather data. The analysis unit can also apply an economic analysis algorithm to poverty data. For example, the analysis unit uses an economic analysis algorithm to analyze poverty data. In this way, applying different analysis algorithms depending on the category of data enables highly accurate analysis.

[0040] During analysis, the analysis unit can determine the priority of analysis 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 evaluates the time when the data was collected and prioritizes analyzing the most recent data. The analysis unit can also postpone analyzing older data. For example, the analysis unit evaluates the time when the data was collected and prioritizes analyzing the older data. In this way, by determining the priority of analysis based on the time when the data was collected, the most recent data can be prioritized for analysis.

[0041] 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 analysis of less relevant data. For example, the analysis unit evaluates the relevance of the data and analyzes less relevant data later. In this way, adjusting the order of analysis based on the relevance of the data enables efficient analysis.

[0042] The proposal unit can adjust the level of detail of the proposal based on the importance of the solution when making a proposal. For example, the proposal unit makes a detailed proposal for a solution with a high level of importance. For example, the proposal unit evaluates the importance of the solution and makes a detailed proposal for the solution with a high level of importance. The proposal unit can also make a simplified proposal for a solution with a low level of importance. For example, the proposal unit evaluates the importance of the solution and makes a simplified proposal for the solution with a low level of importance. In this way, by adjusting the level of detail of the proposal based on the importance of the solution, efficient proposals are possible.

[0043] When making a proposal, the proposal unit can apply different proposal algorithms depending on the category of the solution. For example, the proposal unit applies a weather forecasting algorithm to a weather problem. For example, the proposal unit uses a weather forecasting algorithm to solve the weather problem. The proposal unit can also apply an economic analysis algorithm to a poverty problem. For example, the proposal unit uses an economic analysis algorithm to solve the poverty problem. This enables highly accurate proposals by applying different proposal algorithms depending on the category of the solution.

[0044] When making a proposal, the proposal unit can determine the priority of the proposals based on the time of submission of the solutions. The proposal unit, for example, preferentially proposes solutions with high urgency. For example, the proposal unit evaluates the time of submission of the solutions and preferentially proposes solutions with high urgency. The proposal unit can also postpone proposals of solutions with low urgency. For example, the proposal unit evaluates the time of submission of the solutions and proposes solutions with low urgency at a later date. In this way, by determining the priority of the proposals based on the time of submission of the solutions, it is possible to preferentially propose solutions with high urgency.

[0045] The proposal unit can adjust the order of proposals based on the relevance of the solutions when making proposals. For example, the proposal unit preferentially proposes highly relevant solutions. For example, the proposal unit evaluates the relevance of the solutions and preferentially proposes highly relevant solutions. The proposal unit can also postpone proposing less relevant solutions. For example, the proposal unit evaluates the relevance of the solutions and proposes less relevant solutions later. In this way, adjusting the order of proposals based on the relevance of the solutions enables efficient proposals.

[0046] 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 evaluates the efficiency of the execution method. The execution unit can also analyze the past execution history and identify areas for improvement in the execution method. For example, the execution unit optimizes the execution method based on the past execution history. This makes it possible to select the optimal execution method by analyzing the past execution history and achieve efficient execution.

[0047] The execution unit can customize the means of execution based on the current situation at the time of execution. The execution unit, for example, selects the optimal means of execution based on the current situation. For example, the execution unit evaluates the current situation and selects the optimal means of execution. The execution unit can also customize the means of execution taking the current situation into consideration. For example, the execution unit evaluates the current situation and customizes the means of execution. In this way, optimal execution is possible by customizing the means of execution based on the current situation.

[0048] At the time of execution, the execution unit can select an appropriate execution method taking into consideration geographical location information. The execution unit selects the optimal execution method based on, for example, a current location. For example, the execution unit identifies the current location based on GPS data and selects the optimal execution method. The execution unit can also prioritize execution methods with high geographical relevance. For example, the execution unit evaluates geographical relevance and prioritizes execution methods with high relevance. This enables efficient execution by selecting the optimal execution method taking into consideration geographical location information.

[0049] During execution, the execution unit can analyze social media activity and suggest a means of action. For example, the execution unit analyzes trends on social media and suggests related means of action. For example, the execution unit analyzes the content of social media posts, identifies trends, and suggests related means of action. The execution unit can also select a means of action based on the user's social media activity. For example, the execution unit analyzes the content of the user's posts and reactions, and suggests highly relevant means of action. In this way, highly relevant means of action can be efficiently suggested by analyzing social media activity.

[0050] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0051] The collection unit collects the user's health data, and the analysis unit analyzes the collected health data to predict the user's health condition. For example, the collection unit acquires data such as the user's heart rate, blood pressure, and sleep patterns. The analysis unit can also predict the user's health risks and suggest preventive measures based on the collected health data. This makes it possible to monitor the user's health condition and detect health risks early.

[0052] The suggestion unit can analyze the user's past selection history and suggest solutions based on the user's preferences. For example, the suggestion unit can suggest similar solutions based on solutions the user has previously selected. The suggestion unit can also suggest new solutions based on the user's selection history. This makes it possible to provide solutions that match the user's preferences.

[0053] The collection unit can select data collection targets based on the user's hobbies and interests. For example, if the user is interested in sports, sports-related data will be collected preferentially. The collection unit can also adjust the frequency of data collection based on the user's areas of interest. This makes it possible to collect data according to the user's interests.

[0054] The suggestion unit can collect user feedback and improve the accuracy of suggestions. For example, it can collect how the user responded to a suggestion and reflect that in the next suggestion. The suggestion unit can also improve the content of the suggestion based on the user feedback. This makes it possible to make suggestions that meet the user's needs.

[0055] When collecting data, the collection unit can analyze the user's behavioral patterns and select the optimal collection timing. For example, it can collect data when the user is active and refrain from collecting data when the user is resting. The collection unit can also adjust the frequency of data collection based on the user's behavioral patterns. This makes it possible to collect data that takes user behavior into consideration.

[0056] The processing flow of the first embodiment will be briefly explained below.

[0057] Step 1: The collection unit collects data. For example, the collection unit collects information such as meteorological data and poverty data. Specifically, to collect meteorological data, data such as temperature, precipitation, and wind speed are obtained, and to collect poverty data, data such as income, standard of living, and education level are obtained. Step 2: The analysis unit analyzes the data collected by the collection unit. For example, the analysis unit analyzes the collected weather data and predicts the occurrence of abnormal weather. The analysis unit can analyze the data using statistical analysis or machine learning algorithms. Step 3: The proposal unit proposes solutions based on the analysis results obtained by the analysis unit. For example, the proposal unit may propose financial assistance or educational programs to address poverty issues based on the analysis results. The proposal unit may propose technical or policy solutions. Step 4: The implementation department implements the solutions proposed by the proposal department. For example, the implementation department creates a plan to implement the proposed solutions and takes specific measures. The implementation department can remove pollutants or introduce renewable energy.

[0058] (Example 2) A program management system according to an embodiment of the present invention is a unified management system for global issues (weather, poverty, conflict, and environmental pollution). This program management system provides unified management from event prediction to resolution. Specifically, it utilizes big data to collect information on issues such as weather, poverty, conflict, and environmental pollution, and analyzes the collected data to predict the occurrence of events. For example, it can analyze weather data to predict the occurrence of abnormal weather. Next, it proposes solutions to the predicted events, provides and receives funding to implement the proposed solutions, and creates an implementation plan. For example, in the case of poverty, it proposes financial assistance and educational programs. Furthermore, it takes specific measures to solve the problems based on the implementation plan. For example, in the case of environmental pollution, it removes pollutants and introduces renewable energy. In this way, a unified management system for global issues is established, and problem solving is approached with strong information and response capabilities. As a result, the program management system realizes unified management of global issues and can approach problem solving with strong information and response capabilities.

[0059] A program management system according to an embodiment includes a collection unit, an analysis unit, a proposal unit, and an execution unit. The collection unit collects data. The collection unit collects information such as weather data and poverty data. For example, the collection unit acquires data such as temperature, precipitation, and wind speed to collect weather data. The collection unit can also acquire data such as income, living standards, and education levels to collect poverty data. The analysis unit analyzes the data collected by the collection unit. For example, the analysis unit analyzes the collected weather data and predicts the occurrence of abnormal weather. The analysis unit can analyze the data using, for example, statistical analysis or a machine learning algorithm. The proposal unit proposes solutions based on analysis results obtained by the analysis unit. For example, the proposal unit proposes financial assistance or educational programs to address poverty issues based on the analysis results. The proposal unit can propose, for example, technical or policy solutions. The execution unit implements the solutions proposed by the proposal unit. For example, the execution unit formulates plans for implementing the proposed solutions and takes specific measures. The execution unit can, for example, remove pollutants or introduce renewable energy. As a result, the program management system according to the embodiment can realize centralized management of global issues and can tackle problem solving with strong information and response capabilities.

[0060] The collection unit may collect weather data or poverty data. For example, to collect weather data, the collection unit acquires data such as temperature, precipitation, and wind speed. For example, the collection unit acquires data in real time from weather stations and stores it in a weather database. The collection unit may also acquire data such as income, living standards, and education levels to collect poverty data. For example, the collection unit collects statistical data provided by government agencies and non-governmental organizations and stores it in a poverty database. In this way, by collecting information such as weather data and poverty data, comprehensive data on global issues can be obtained.

[0061] The analysis unit can analyze the collected data and predict the occurrence of events. The analysis unit can, for example, analyze collected weather data and predict the occurrence of abnormal weather. For example, the analysis unit can analyze the data using statistical analysis or machine learning algorithms. For example, the analysis unit can calculate the probability of abnormal weather occurring based on the weather data and provide the prediction result. The analysis unit can also analyze the collected poverty data and predict the occurrence of poverty issues. For example, the analysis unit can calculate the probability of poverty occurring based on data on income and living standards and provide the prediction result. In this way, by analyzing the collected data and predicting the occurrence of events, it becomes possible to detect and respond to problems early.

[0062] The proposal unit can propose solutions based on the analysis results. For example, the proposal unit can propose financial assistance or educational programs to address poverty issues based on the analysis results. For example, the proposal unit can provide educational programs to poor areas to improve living standards. The proposal unit can also propose solutions to environmental pollution issues based on the analysis results. For example, the proposal unit can propose the removal of pollutants or the introduction of renewable energy. In this way, by proposing solutions based on the analysis results, appropriate countermeasures can be provided quickly.

[0063] The implementation unit can make a plan to implement the proposed solution and take specific measures. The implementation unit, for example, makes a plan to implement the proposed solution and takes specific measures. For example, the implementation unit can make a plan to remove pollutants and actually remove the pollutants. The implementation unit can also make a plan to introduce renewable energy and actually introduce the renewable energy. In this way, by making a plan to implement the proposed solution and taking specific measures, the ability to implement problem solving is improved.

[0064] The collection unit can estimate the user's emotions and adjust the timing of data collection based on the user's 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, the collection unit analyzes the user's emotion data and reduces the frequency of data collection if the stress level is high. The collection unit can also increase the frequency of data collection to collect more detailed information if the user is relaxed. For example, the collection unit analyzes the user's emotion data and increases the frequency of data collection if the relaxation level is high. In this way, by adjusting the timing of data collection according to the user's emotions, the burden on the user can be reduced and efficient data collection is possible.

[0065] The collection unit can analyze past data collection history and select an appropriate collection method. The collection unit, for example, selects the most efficient collection method from past data collection history. For example, the collection unit analyzes past data collection history and evaluates the efficiency of the collection method. The collection unit can also analyze past data collection history and identify areas for improvement in the collection method. For example, the collection unit optimizes the collection method based on past data collection history. In this way, by analyzing past data collection history, the optimal collection method can be selected, enabling efficient data collection.

[0066] The collection unit can perform filtering based on the current geographical situation and areas of interest when collecting data. The collection unit, for example, prioritizes collecting highly relevant data based on the current geographical situation. For example, the collection unit analyzes the current geographical situation and filters out highly relevant data. The collection unit can also filter the data to be collected based on the user's areas of interest. For example, the collection unit analyzes the user's areas of interest and prioritizes collecting highly relevant data. In this way, highly relevant data can be efficiently collected by filtering data based on the current geographical situation and areas of interest.

[0067] The collection unit can estimate the user's emotions and determine the priority of data to be collected based on the user's emotions. For example, if the user is feeling stressed, the collection unit postpones collection of less important data. For example, the collection unit analyzes the user's emotion data and postpones collection of less important data if the stress level is high. The collection unit can also prioritize collection of detailed data if the user is relaxed. For example, the collection unit analyzes the user's emotion data and prioritizes collection of detailed data if the relaxation level is high. In this way, by determining the priority of data to be collected according to the user's emotions, important data can be collected preferentially.

[0068] 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 information close to the current location. For example, the collection unit identifies the current location based on GPS data and prioritizes collecting highly relevant data. The collection unit can also prioritize collecting data that is highly geographically relevant. For example, the collection unit evaluates geographical relevance and prioritizes collecting highly relevant data. This enables efficient data collection by prioritizing the collection of highly relevant data by taking geographical location information into consideration.

[0069] The collection unit can analyze social media activity and collect relevant data when collecting data. The collection unit, for example, analyzes trends on social media and collects relevant data. For example, the collection unit analyzes the content of social media posts, identifies trends, and collects relevant data. The collection unit can also select data to collect based on the user's social media activity. For example, the collection unit analyzes the content of users' posts and reactions to collect highly relevant data. In this way, highly relevant data can be efficiently collected by analyzing social media activity.

[0070] The analysis unit can estimate the user's emotions and adjust the way the analysis is presented based on the user's emotions. For example, if the user is nervous, the analysis unit provides simple, highly visible analysis results. For example, the analysis unit analyzes the user's emotional data and displays the analysis results in simple graphs or text when the level of tension is high. The analysis unit can also provide detailed analysis results when the user is relaxed. For example, the analysis unit analyzes the user's emotional data and provides detailed analysis results when the level of relaxation is high. In this way, by adjusting the way the analysis is presented based on the user's emotions, it is possible to provide analysis results that are easy for the user to understand.

[0071] 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. This allows for efficient analysis by adjusting the level of detail of the analysis based on the importance of the data.

[0072] During analysis, the analysis unit can apply different analysis algorithms depending on the category of data. For example, the analysis unit applies a weather forecasting algorithm to weather data. For example, the analysis unit uses a weather forecasting algorithm to analyze weather data. The analysis unit can also apply an economic analysis algorithm to poverty data. For example, the analysis unit uses an economic analysis algorithm to analyze poverty data. In this way, applying different analysis algorithms depending on the category of data enables highly accurate analysis.

[0073] The analysis unit can estimate the user's emotions and adjust the length of the analysis based on the user's emotions. For example, if the user is in a hurry, the analysis unit provides a short and to-the-point analysis result. For example, the analysis unit analyzes the user's emotion data and provides a short summary if the user is in a hurry. The analysis unit can also provide a detailed analysis result if the user is relaxed. For example, the analysis unit analyzes the user's emotion data and provides a detailed analysis result if the user is relaxed. In this way, by adjusting the length of the analysis according to the user's emotions, it is possible to provide an analysis result that is appropriate for the user.

[0074] During analysis, the analysis unit can determine the priority of analysis 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 evaluates the time when the data was collected and prioritizes analyzing the most recent data. The analysis unit can also postpone analyzing older data. For example, the analysis unit evaluates the time when the data was collected and prioritizes analyzing the older data. In this way, by determining the priority of analysis based on the time when the data was collected, the most recent data can be prioritized for analysis.

[0075] 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 analysis of less relevant data. For example, the analysis unit evaluates the relevance of the data and analyzes less relevant data later. In this way, adjusting the order of analysis based on the relevance of the data enables efficient analysis.

[0076] The suggestion unit can estimate the user's emotions and adjust the way in which suggestions are expressed based on the user's emotions. For example, if the user is nervous, the suggestion unit provides simple, highly visible suggestions. For example, the suggestion unit analyzes the user's emotion data and displays suggestions in the form of simple graphs or text when the tension level is high. The suggestion unit can also provide detailed suggestions when the user is relaxed. For example, the suggestion unit analyzes the user's emotion data and provides detailed suggestions when the relaxation level is high. In this way, by adjusting the way in which suggestions are expressed according to the user's emotions, it is possible to provide suggestions that are easy for the user to understand.

[0077] The proposal unit can adjust the level of detail of the proposal based on the importance of the solution when making a proposal. For example, the proposal unit makes a detailed proposal for a solution with a high level of importance. For example, the proposal unit evaluates the importance of the solution and makes a detailed proposal for the solution with a high level of importance. The proposal unit can also make a simplified proposal for a solution with a low level of importance. For example, the proposal unit evaluates the importance of the solution and makes a simplified proposal for the solution with a low level of importance. In this way, by adjusting the level of detail of the proposal based on the importance of the solution, efficient proposals are possible.

[0078] When making a proposal, the proposal unit can apply different proposal algorithms depending on the category of the solution. For example, the proposal unit applies a weather forecasting algorithm to a weather problem. For example, the proposal unit uses a weather forecasting algorithm to solve the weather problem. The proposal unit can also apply an economic analysis algorithm to a poverty problem. For example, the proposal unit uses an economic analysis algorithm to solve the poverty problem. This enables highly accurate proposals by applying different proposal algorithms depending on the category of the solution.

[0079] The suggestion unit can estimate the user's emotions and adjust the length of the suggestion based on the user's emotions. For example, if the user is in a hurry, the suggestion unit provides a short and to-the-point suggestion. For example, the suggestion unit analyzes the user's emotion data and provides a short summary if the user is in a hurry. The suggestion unit can also provide a detailed suggestion if the user is relaxed. For example, the suggestion unit analyzes the user's emotion data and provides a detailed suggestion if the user is relaxed. In this way, the length of the suggestion can be adjusted according to the user's emotions, thereby providing a suggestion appropriate for the user.

[0080] When making a proposal, the proposal unit can determine the priority of the proposals based on the time of submission of the solutions. The proposal unit, for example, preferentially proposes solutions with high urgency. For example, the proposal unit evaluates the time of submission of the solutions and preferentially proposes solutions with high urgency. The proposal unit can also postpone proposals of solutions with low urgency. For example, the proposal unit evaluates the time of submission of the solutions and proposes solutions with low urgency at a later date. In this way, by determining the priority of the proposals based on the time of submission of the solutions, it is possible to preferentially propose solutions with high urgency.

[0081] The proposal unit can adjust the order of proposals based on the relevance of the solutions when making proposals. For example, the proposal unit preferentially proposes highly relevant solutions. For example, the proposal unit evaluates the relevance of the solutions and preferentially proposes highly relevant solutions. The proposal unit can also postpone proposing less relevant solutions. For example, the proposal unit evaluates the relevance of the solutions and proposes less relevant solutions later. In this way, adjusting the order of proposals based on the relevance of the solutions enables efficient proposals.

[0082] The execution unit can estimate the user's emotions and adjust the execution method based on the user's emotions. For example, if the user is nervous, the execution unit provides a simple and highly visible execution method. For example, the execution unit analyzes the user's emotional data and provides an execution method with simple steps when the tension level is high. The execution unit can also provide a detailed execution method when the user is relaxed. For example, the execution unit analyzes the user's emotional data and provides an execution method with detailed steps when the relaxation level is high. In this way, the execution method can be adjusted according to the user's emotions, thereby providing an execution method that is appropriate for the user.

[0083] 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 evaluates the efficiency of the execution method. The execution unit can also analyze the past execution history and identify areas for improvement in the execution method. For example, the execution unit optimizes the execution method based on the past execution history. This makes it possible to select the optimal execution method by analyzing the past execution history and achieve efficient execution.

[0084] The execution unit can customize the means of execution based on the current situation at the time of execution. The execution unit, for example, selects the optimal means of execution based on the current situation. For example, the execution unit evaluates the current situation and selects the optimal means of execution. The execution unit can also customize the means of execution taking the current situation into consideration. For example, the execution unit evaluates the current situation and customizes the means of execution. In this way, optimal execution is possible by customizing the means of execution based on the current situation.

[0085] The execution unit can estimate the user's emotions and determine the priority of execution based on the user's emotions. For example, if the user is feeling stressed, the execution unit postpones execution of less important tasks. For example, the execution unit analyzes the user's emotion data and postpones execution of less important tasks when the stress level is high. The execution unit can also prioritize detailed execution when the user is relaxed. For example, the execution unit analyzes the user's emotion data and prioritizes detailed execution when the relaxation level is high. In this way, by determining the priority of execution according to the user's emotions, important execution can be prioritized.

[0086] At the time of execution, the execution unit can select an appropriate execution method taking into consideration geographical location information. The execution unit selects the optimal execution method based on, for example, a current location. For example, the execution unit identifies the current location based on GPS data and selects the optimal execution method. The execution unit can also prioritize execution methods with high geographical relevance. For example, the execution unit evaluates geographical relevance and prioritizes execution methods with high relevance. This enables efficient execution by selecting the optimal execution method taking into consideration geographical location information.

[0087] During execution, the execution unit can analyze social media activity and suggest a means of action. For example, the execution unit analyzes trends on social media and suggests related means of action. For example, the execution unit analyzes the content of social media posts, identifies trends, and suggests related means of action. The execution unit can also select a means of action based on the user's social media activity. For example, the execution unit analyzes the content of the user's posts and reactions, and suggests highly relevant means of action. In this way, highly relevant means of action can be efficiently suggested by analyzing social media activity. === Hard Collateral 1-1 === Each of the multiple elements, including the collection unit, analysis unit, proposal unit, and execution unit, described above, is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the collection unit collects weather data and poverty data using the camera 42 and sensors of the smart device 14 and acquires the data using the control unit 46A. The analysis unit, realized, for example, by the specific processing unit 290 of the data processing device 12, analyzes the collected data and predicts the occurrence of abnormal weather. The proposal unit, realized, for example, by the specific processing unit 290 of the data processing device 12, proposes solutions based on the analysis results. The execution unit, realized, for example, by the control unit 46A of the smart device 14, makes a plan for implementing the proposed solutions and takes specific measures. === Hard Collateral 1-2 === Each of the multiple elements, including the collection unit, analysis unit, proposal unit, and execution 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 weather data and poverty data using the camera 42 and sensors of the smart glasses 214 and acquires the data using the control unit 46A. The analysis unit, realized, for example, by the specific processing unit 290 of the data processing device 12, analyzes the collected data and predicts the occurrence of abnormal weather. The proposal unit, realized, for example, by the specific processing unit 290 of the data processing device 12, proposes solutions based on the analysis results. The execution unit, realized, for example, by the control unit 46A of the smart glasses 214, makes a plan for implementing the proposed solutions and takes specific measures. === Hard Collateral 1-3 === Each of the multiple elements including the collection unit, analysis unit, proposal unit, and execution unit described above is realized, for example, by at least one of the headset-type terminal 314 and the data processing device 12. For example, the collection unit collects weather data and poverty data using the camera 42 and sensors of the headset-type terminal 314 and acquires the data using the control unit 46A. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the collected data to predict the occurrence of abnormal weather. The proposal unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and proposes solutions based on the analysis results. The execution unit is realized, for example, by the control unit 46A of the headset-type terminal 314 and makes a plan for implementing the proposed solutions and takes specific measures. === Hard Collateral 1-4 === Each of the multiple elements including the collection unit, analysis unit, proposal unit, and execution unit described above is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the collection unit collects weather data and poverty data using the camera 42 and sensors of the robot 414 and acquires the data using the control unit 46A. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the collected data to predict the occurrence of abnormal weather. The proposal unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and proposes solutions based on the analysis results. The execution unit is realized, for example, by the control unit 46A of the robot 414 and makes a plan to implement the proposed solutions and takes specific measures.

[0088] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0089] The collection unit collects the user's health data, and the analysis unit analyzes the collected health data to predict the user's health condition. For example, the collection unit acquires data such as the user's heart rate, blood pressure, and sleep patterns. The analysis unit can also predict the user's health risks and suggest preventive measures based on the collected health data. This makes it possible to monitor the user's health condition and detect health risks early.

[0090] The analysis unit can estimate the user's emotions and adjust the notification method of the analysis results based on the user's emotions. For example, if the user is feeling stressed, the analysis results can be notified briefly, and if the user is relaxed, detailed analysis results can be provided. The analysis unit can also adjust the timing of notification according to the user's emotions. This makes it possible to notify the analysis results in consideration of the user's emotions.

[0091] The suggestion unit can analyze the user's past selection history and suggest solutions based on the user's preferences. For example, the suggestion unit can suggest similar solutions based on solutions the user has previously selected. The suggestion unit can also suggest new solutions based on the user's selection history. This makes it possible to provide solutions that match the user's preferences.

[0092] The execution unit can estimate the user's emotions and adjust the timing of execution based on the user's emotions. For example, if the user is feeling stressed, the execution can be delayed, and if the user is relaxed, the execution can be accelerated. The execution unit can also simplify the execution procedure according to the user's emotions. This allows for execution that takes the user's emotions into consideration.

[0093] The collection unit can select data collection targets based on the user's hobbies and interests. For example, if the user is interested in sports, sports-related data will be collected preferentially. The collection unit can also adjust the frequency of data collection based on the user's areas of interest. This makes it possible to collect data according to the user's interests.

[0094] The analysis unit can estimate the user's emotions and adjust the accuracy of the analysis based on the user's emotions. For example, if the user is nervous, the analysis accuracy is increased, and if the user is relaxed, the analysis accuracy is decreased. The analysis unit can also change the analysis method depending on the user's emotions. This enables analysis that takes the user's emotions into consideration.

[0095] The suggestion unit can collect user feedback and improve the accuracy of suggestions. For example, it can collect how the user responded to a suggestion and reflect that in the next suggestion. The suggestion unit can also improve the content of the suggestion based on the user feedback. This makes it possible to make suggestions that meet the user's needs.

[0096] The execution unit can estimate the user's emotions and select an execution method based on the user's emotions. For example, if the user is feeling stressed, a simple method is selected, and if the user is relaxed, a more detailed method is selected. The execution unit can also change the execution procedure depending on the user's emotions. This allows for execution that takes the user's emotions into consideration.

[0097] When collecting data, the collection unit can analyze the user's behavioral patterns and select the optimal collection timing. For example, it can collect data when the user is active and refrain from collecting data when the user is resting. The collection unit can also adjust the frequency of data collection based on the user's behavioral patterns. This makes it possible to collect data that takes user behavior into consideration.

[0098] The analysis unit can estimate the user's emotions and adjust the way the analysis results are displayed based on the user's emotions. For example, if the user is nervous, the analysis results can be displayed as simple graphs or text, and if the user is relaxed, the analysis results can be displayed as detailed graphs or text. The analysis unit can also adjust the timing of displaying the analysis results depending on the user's emotions. This makes it possible to display analysis results that take the user's emotions into consideration.

[0099] The processing flow of the second embodiment will be briefly explained below.

[0100] Step 1: The collection unit collects data. For example, the collection unit collects information such as meteorological data and poverty data. Specifically, to collect meteorological data, data such as temperature, precipitation, and wind speed are obtained, and to collect poverty data, data such as income, standard of living, and education level are obtained. Step 2: The analysis unit analyzes the data collected by the collection unit. For example, the analysis unit analyzes the collected weather data and predicts the occurrence of abnormal weather. The analysis unit can analyze the data using statistical analysis or machine learning algorithms. Step 3: The proposal unit proposes solutions based on the analysis results obtained by the analysis unit. For example, the proposal unit may propose financial assistance or educational programs to address poverty issues based on the analysis results. The proposal unit may propose technical or policy solutions. Step 4: The implementation department implements the solutions proposed by the proposal department. For example, the implementation department creates a plan to implement the proposed solutions and takes specific measures. The implementation department can remove pollutants or introduce renewable energy.

[0101] 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.

[0102] 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.

[0103] 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.

[0104] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0105] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0106] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0107] 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.

[0108] 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.

[0109] 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.

[0110] 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).

[0111] 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.

[0112] 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.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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.

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

[0121] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0122] 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.

[0123] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0124] The 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.

[0125] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0126] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0127] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0128] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0129] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0130] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0131] In the 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.

[0132] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0133] The specific processing unit 290 transmits the result of the specific processing to the 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.

[0134] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt 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.

[0135] 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.

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

[0137] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0138] 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.

[0139] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0140] The 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.

[0141] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0142] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS 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).

[0143] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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).

[0158] 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.

[0159] 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."

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] [Explanation of symbols]

[0173] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a collection unit that collects data; an analysis unit that analyzes the data collected by the collection unit; Based on the analysis results obtained by the aforementioned analysis unit, a proposal unit proposes a solution, The system comprises an execution unit that executes the solution proposed by the aforementioned proposal unit. A system characterized by:

2. The collecting unit Collect weather or poverty data The system of claim 1 .

3. The analysis unit Analyze collected data and predict the occurrence of events The system of claim 1 .

4. The proposal unit Propose solutions based on analysis results The system of claim 1 .

5. The execution unit: Develop a plan to implement the proposed solutions and take concrete action The system of claim 1 .

6. The collecting unit Infer user emotions and adjust data collection timing based on user emotions The system of claim 1 .

7. The collecting unit Analyze past data collection history and select the appropriate collection method. The system of claim 1 .

8. The collecting unit Filter data collection based on current geography and areas of interest The system of claim 1 .

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