system

Abstract

The system according to the embodiment aims to utilize the location information of the user to provide the latest information on CO2 reduction activities and encourage the user to take action. [Solution] A system according to an embodiment includes an acquisition unit, a provision unit, and a management unit. The acquisition unit acquires location information of a user. The provision unit provides the latest information on CO2 reduction activities based on the location information acquired by the acquisition unit. The management unit tracks user behavior and manages rewards based on the information provided by the provision 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 technology had the problem of not having a sufficient system in place to utilize users' location information to provide the latest information on CO2 reduction activities and encourage users to take action.

[0005] The system according to the embodiment aims to utilize the location information of the user to provide the latest information on CO2 reduction activities and encourage the user to take action. [Means for solving the problem]

[0006] The system according to the embodiment includes an acquisition unit, a provision unit, and a management unit. The acquisition unit acquires location information of a user. The provision unit provides the latest information on CO2 reduction activities based on the location information acquired by the acquisition unit. The management unit tracks user behavior and manages rewards based on the information provided by the provision unit. [Effects of the Invention]

[0007] The system according to the embodiment utilizes the location information of the user to provide the latest information on CO2 reduction activities, thereby encouraging the user to take action. [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 system according to an embodiment of the present invention utilizes a generation AI to provide the latest information on global CO2 reduction activities in conjunction with a user's location information. This system acquires the user's location information, and the generation AI provides the latest information on CO2 reduction activities based on the acquired location information. The system also includes a mechanism for rewarding users based on their contributions to CO2 reduction. For example, the system acquires the user's location information and inputs it into the generation AI. The generation AI analyzes the input location information and provides the user with the latest information on CO2 reduction activities relevant to the user. For example, the system can provide information on tree planting activities and energy efficiency projects taking place in the user's area. Furthermore, the system also includes a mechanism for rewarding users based on their contributions to CO2 reduction. For example, users can earn points by using public transportation or eco-bags and receive rewards based on those points. These rewards can be provided in the form of electronic money or coupons. This encourages users to take voluntary action and raises awareness of addressing global warming issues. The system provides the latest information on CO2 reduction activities based on the user's location information, tracks the user's behavior, and manages rewards. For example, users can receive rewards while experiencing how their actions contribute to CO2 reduction.

[0029] A CO2 reduction activity information provision system according to an embodiment includes an acquisition unit, a provision unit, and a management unit. The acquisition unit acquires user location information. For example, the acquisition unit can identify the user's current location using GPS data. The acquisition unit can also identify the user's location using Wi-Fi location information. The acquisition unit can also identify the user's location using cell tower information. The provision unit uses a generation AI to provide the latest information on CO2 reduction activities based on the location information acquired by the acquisition unit. For example, the provision unit causes the generation AI to reference a database of CO2 reduction activities around the world and select optimal information based on the user's location information. The generation AI can generate information using, for example, a machine learning model or a natural language generation model. The management unit tracks user behavior and manages rewards based on the information provided by the provision unit. For example, the management unit can verify a user's actions when using public transportation or an eco-bag using a two-dimensional code (e.g., a QR code (registered trademark)) or GPS data. The management unit can also accumulate points based on the user's contribution to CO2 reduction and provide the points as rewards. As a result, the CO2 reduction activity information providing system according to the embodiment can provide the latest information on CO2 reduction activities based on the user's location information, and can track the user's behavior to manage rewards.

[0030] The acquisition unit can identify the user's current location and input the location information to the generation AI. The acquisition unit can identify the user's current location using, for example, GPS data. For example, the acquisition unit can acquire location information using the GPS function of the user's smartphone. The acquisition unit can also identify the user's location using Wi-Fi location information. For example, the acquisition unit can collect information on surrounding Wi-Fi access points and identify the location based on that information. Furthermore, the acquisition unit can also identify the user's location using cell tower information. For example, the acquisition unit can identify the location based on information about the cell tower to which the user's smartphone is connected. In this way, the user's current location can be identified and input to the generation AI, thereby providing optimal information based on the location information.

[0031] The providing unit can use the generation AI to refer to a database of CO2 reduction activities around the world and select appropriate information based on the user's location information. For example, the generating AI can refer to a database of CO2 reduction activities around the world and select optimal information based on the user's location information. The generating AI can, for example, analyze the database using a machine learning model to extract relevant information. The generating AI can also generate information to be provided to the user using a natural language generation model. For example, the generating AI can provide information such as tree planting activities and energy efficiency projects being carried out in the user's area. The database includes, for example, types of CO2 reduction activities and activity information by region. This allows the generating AI to refer to the database and provide optimal information based on the user's location information.

[0032] The management unit can certify a user's actions when using public transportation or an eco-bag using two-dimensional codes or GPS data. For example, when a user uses public transportation, the management unit scans a two-dimensional code to certify the user's actions. For example, the management unit scans a two-dimensional code issued when boarding a bus or train and records the user's actions. The management unit can also certify a user's actions when using an eco-bag by scanning a two-dimensional code. For example, the management unit scans a two-dimensional code attached to the eco-bag and records the user's actions. Furthermore, the management unit can certify a user's actions using GPS data. For example, the management unit uses the GPS function of the user's smartphone to confirm that the user is in a specific location. This enables accurate tracking by certifying the user's actions using two-dimensional codes or GPS data.

[0033] The management unit can accumulate points based on the amount of CO2 reduction a user contributes to and provide the points as a reward. For example, the management unit can award points when a user uses public transportation. For example, the management unit can scan a two-dimensional code issued when boarding a bus or train and award the points. The management unit can also award points when a user uses an eco-bag. For example, the management unit can scan a two-dimensional code attached to the eco-bag and award the points. Furthermore, the management unit can award points for other actions that contribute to CO2 reduction by the user. For example, the management unit can award points when a user participates in recycling activities. Points are awarded based on the amount of CO2 reduction, for example. For example, the management unit can calculate and award points based on the amount of CO2 reduction a user has achieved. In this way, by awarding points for the user's CO2 reduction activities and providing them as a reward, it is possible to increase the user's motivation.

[0034] The provision unit can provide information on tree planting activities and energy efficiency projects being carried out in the user's area. For example, the generation AI provides information on tree planting activities being carried out in the user's area based on the user's location information. For example, the provision unit provides information on local tree planting events and volunteer activities. The provision unit can also provide information on energy efficiency projects being carried out in the user's area. For example, the provision unit provides information on improving local energy efficiency and introducing renewable energy. This makes it easier to attract the user's interest by providing information on CO2 reduction activities specific to the user's area.

[0035] The acquisition unit can analyze the user's past location information history and select an appropriate acquisition method. The acquisition unit, for example, adjusts the frequency of acquiring location information based on places the user has frequently visited in the past. For example, the acquisition unit analyzes the user's past location information history, and if the user frequently visits a particular place, increases the frequency of acquiring location information at that place. The acquisition unit can also analyze the user's movement pattern and acquire location information at the optimal timing. For example, the acquisition unit analyzes the user's movement pattern and acquires location information during times of heavy movement. Furthermore, the acquisition unit can predict places the user will visit during a particular time period and acquire location information during that time period. For example, the acquisition unit predicts places the user will visit during a particular time period based on the user's past location information history and acquires location information during that time period. In this way, the optimal location information acquisition method can be selected by analyzing the past location information history.

[0036] When acquiring location information, the acquisition unit can perform filtering based on the user's current activity status and areas of interest. For example, if the user is shopping, the acquisition unit prioritizes acquiring related CO2 reduction activity information. For example, the acquisition unit analyzes the user's current activity status, detects that the user is shopping, and acquires CO2 reduction activity information related to shopping. The acquisition unit can also prioritize acquiring information about energy efficiency if the user is playing sports. For example, the acquisition unit analyzes the user's current activity status, detects that the user is playing sports, and acquires information about energy efficiency. Furthermore, if the user is traveling, the acquisition unit can prioritize acquiring CO2 reduction activity information for the visited destination. For example, the acquisition unit analyzes the user's current activity status, detects that the user is traveling, and acquires CO2 reduction activity information for the visited destination. This allows more relevant information to be provided by filtering information based on the user's activity status and areas of interest.

[0037] When acquiring location information, the acquisition unit can select an appropriate acquisition means depending on the user's input method. For example, if the user is using voice input, the acquisition unit acquires location information using voice recognition technology. For example, the acquisition unit analyzes the user's voice input and identifies location information. Furthermore, if the user is using text input, the acquisition unit can also acquire location information using text analysis technology. For example, the acquisition unit analyzes the user's text input and identifies location information. Furthermore, if the user is using image input, the acquisition unit can also acquire location information using image analysis technology. For example, the acquisition unit analyzes an image taken by the user and identifies location information. This improves the accuracy of acquiring location information by selecting the optimal acquisition means depending on the user's input method.

[0038] When acquiring location information, the acquisition unit can prioritize acquiring highly relevant information by taking into account the user's geographical location information. For example, if the user is in an urban area, the acquisition unit prioritizes acquiring information about CO2 reduction activities being carried out in that city. For example, the acquisition unit analyzes the user's geographical location information and acquires information about CO2 reduction activities being carried out in the urban area. Furthermore, if the user is in a natural environment, the acquisition unit can also prioritize acquiring information about nature conservation activities. For example, the acquisition unit analyzes the user's geographical location information and acquires information about nature conservation activities being carried out in the natural environment. Furthermore, if the user is in a tourist destination, the acquisition unit can also prioritize acquiring information about tourism-related CO2 reduction activities in that area. For example, the acquisition unit analyzes the user's geographical location information and acquires information about CO2 reduction activities being carried out in the tourist destination. In this way, highly relevant information can be prioritized by taking into account the user's geographical location information.

[0039] When acquiring location information, the acquisition unit can analyze the user's social media activities and acquire related information. The acquisition unit, for example, acquires CO2 reduction activity information related to places where the user has checked in on social media. For example, the acquisition unit analyzes the user's social media activities and acquires information related to the checked-in places. The acquisition unit can also analyze the content of the user's posts on social media and acquire related CO2 reduction activity information. For example, the acquisition unit analyzes the content of the user's posts and acquires related information. Furthermore, the acquisition unit can also acquire related CO2 reduction activity information by referring to the activities of the user's friends on social media. For example, the acquisition unit analyzes the activities of the user's friends and acquires related information. In this way, related information can be acquired by analyzing the user's social media activities.

[0040] When acquiring location information, the acquisition unit can customize the acquisition method by reflecting the user's past feedback. The acquisition unit, for example, preferentially acquires location information from information sources that the user has previously rated highly. For example, the acquisition unit analyzes the user's past feedback and preferentially acquires location information from information sources that the user has previously rated highly. The acquisition unit can also adjust the acquisition method based on feedback provided by the user in the past. For example, the acquisition unit analyzes the user's feedback and customizes the acquisition method. Furthermore, the acquisition unit can analyze the user's past feedback and suggest an optimal acquisition method. For example, the acquisition unit suggests an optimal acquisition method based on the user's feedback. In this way, the acquisition method can be customized by reflecting the user's past feedback.

[0041] The providing unit can adjust the level of detail of the information based on the importance of the CO2 reduction activity when providing the information. For example, the providing unit provides detailed information for CO2 reduction activities with high importance. For example, the providing unit provides detailed information about CO2 reduction activities with high importance. The providing unit can also provide concise information for CO2 reduction activities with low importance. For example, the providing unit provides concise information about CO2 reduction activities with low importance. Furthermore, the providing unit can gradually adjust the level of detail of the information according to the importance. For example, the providing unit adjusts the level of detail of the information according to the importance and provides it to the user. In this way, by adjusting the level of detail of the information based on the importance of the CO2 reduction activity, it is possible to provide useful information to the user.

[0042] When providing the information, the providing unit can apply different providing algorithms depending on the category of the CO2 reduction activity. For example, the providing unit provides information about tree planting activities using a visual map. For example, the providing unit displays information about tree planting activities using a visual map. The providing unit can also provide information about energy efficiency projects using a data chart. For example, the providing unit displays information about energy efficiency projects using a data chart. Furthermore, the providing unit can also provide information about recycling activities using a video. For example, the providing unit displays information about recycling activities using a video. This makes it possible to provide more appropriate information by adjusting the providing algorithm depending on the category of the CO2 reduction activity.

[0043] When providing information, the providing unit can improve the accuracy of the information provided by referring to the user's past information provision results. The providing unit, for example, preferentially provides information from information sources that the user has given a high rating in the past. For example, the providing unit analyzes the user's past information provision results and preferentially provides information from information sources that the user has given a high rating. The providing unit can also provide optimal information based on the user's past behavioral history. For example, the providing unit analyzes the user's past behavioral history and provides optimal information. Furthermore, the providing unit can analyze the user's past feedback and improve the accuracy of the information provided. For example, the providing unit improves the accuracy of the information provided by referring to the past information provision results.

[0044] The providing unit can determine the priority of information at the time of providing the information based on the timing of CO2 reduction activities. For example, the providing unit prioritizes providing information about CO2 reduction activities that will be implemented most recently. For example, the providing unit prioritizes providing information about tree planting events or energy efficiency projects that will be implemented most recently. The providing unit can also provide information about long-term CO2 reduction projects in stages. For example, the providing unit provides progress reports on long-term energy efficiency projects in stages. Furthermore, the providing unit can adjust the priority of information depending on the timing of implementation. For example, the providing unit adjusts the priority of information depending on the season or the timing of an event and provides it to the user. This enables timely information provision by determining the priority of information based on the timing of CO2 reduction activities.

[0045] The providing unit can adjust the order of information based on the relevance of CO2 reduction activities when providing the information. For example, the providing unit first provides information that is most relevant to the user's current location. For example, the providing unit first provides CO2 reduction activity information related to the user's current location. The providing unit can also postpone less relevant information. For example, the providing unit postpones information that is less relevant to the user's current location. Furthermore, the providing unit can gradually adjust the order of information according to the relevance. For example, the providing unit adjusts the order of information based on the user's level of interest and past behavioral history and provides it to the user. In this way, by adjusting the order of information based on the relevance of CO2 reduction activities, the most relevant information to the user can be provided preferentially.

[0046] The providing unit can adjust the use of technical terms in the information according to the user's level of expertise when providing the information. For example, the providing unit provides information using detailed technical terms to a user with high expertise. For example, the providing unit provides detailed information using technical terms to a user with high expertise. The providing unit can also provide information using concise and easy-to-understand language to a user with low expertise. For example, the providing unit provides information using concise and easy-to-understand language to a user with low expertise. Furthermore, the providing unit can gradually adjust the use of technical terms in the information according to the user's level of expertise. For example, the providing unit adjusts the use of technical terms in the information according to the user's level of expertise and provides it to the user. In this way, by adjusting the use of technical terms in the information according to the user's level of expertise, it is possible to provide information that is easy for the user to understand.

[0047] During management, the management unit can analyze the user's past behavioral history and select the optimal reward management method. For example, the management unit preferentially selects reward management methods that the user has given high ratings to in the past. For example, the management unit analyzes the user's past behavioral history and selects highly rated reward management methods. The management unit can also select the optimal reward management method based on the user's past behavioral history. For example, the management unit analyzes the user's past behavioral history and selects the optimal reward management method. Furthermore, the management unit can analyze the user's past feedback and select the optimal reward management method. For example, the management unit selects the optimal reward management method based on the user's feedback. In this way, the optimal reward management method can be selected by analyzing the user's past behavioral history.

[0048] During management, the management unit can customize the reward management means based on the user's current living situation. For example, when the user is busy, the management unit provides a simple and quick reward management means. For example, the management unit provides a simple and quick reward management means when the user is busy. The management unit can also provide a detailed reward management means when the user is relaxed. For example, the management unit provides a detailed reward management means when the user is relaxed. Furthermore, the management unit can customize the reward management means according to the user's living situation. For example, the management unit analyzes the user's living situation and provides the optimal reward management means. In this way, by customizing the reward management means based on the user's living situation, optimal reward management for the user is possible.

[0049] During management, the management unit can improve the reward management method by reflecting user feedback. The management unit improves the reward management method, for example, based on feedback provided by the user in the past. For example, the management unit analyzes the user's feedback and improves the reward management method. The management unit can also analyze the user's feedback and propose an optimal reward management method. For example, the management unit proposes an optimal reward management method based on the user's feedback. Furthermore, the management unit can reflect the user's feedback and gradually improve the reward management method. For example, the management unit gradually improves the reward management method based on the user's feedback. In this way, the reward management method can be improved by reflecting the user's feedback.

[0050] During management, the management unit can select an optimal reward management method by taking into account the user's geographical location information. For example, if the user is in an urban area, the management unit can prioritize providing rewards that can be used in the city. For example, the management unit can analyze the user's geographical location information and provide rewards that can be used in the urban area. Furthermore, if the user is in a natural environment, the management unit can prioritize providing rewards related to nature conservation activities. For example, the management unit can analyze the user's geographical location information and provide rewards that can be used in the natural environment. Furthermore, if the user is in a tourist destination, the management unit can prioritize providing rewards that can be used in that area. For example, the management unit can analyze the user's geographical location information and provide rewards that can be used in the tourist destination. In this way, the optimal reward management method can be selected by taking into account the user's geographical location information.

[0051] During management, the management unit can analyze the user's social media activity and suggest reward management measures. The management unit, for example, suggests rewards related to places the user has checked in to on social media. For example, the management unit analyzes the user's social media activity and suggests rewards related to places the user has checked in to. The management unit can also analyze the content of the user's posts on social media and suggest related rewards. For example, the management unit analyzes the content of the user's posts and suggests related rewards. Furthermore, the management unit can also suggest related rewards based on the activities of the user's friends on social media. For example, the management unit analyzes the activities of the user's friends and suggests related rewards. In this way, optimal reward management measures can be suggested by analyzing the user's social media activity.

[0052] During management, the management unit can customize the reward management method by reflecting the user's past feedback. For example, the management unit preferentially selects reward management methods that the user has given high ratings to in the past. For example, the management unit analyzes the user's past feedback and selects reward management methods that have received high ratings. The management unit can also adjust the reward management method based on the user's past feedback. For example, the management unit analyzes the user's feedback and customizes the reward management method. Furthermore, the management unit can analyze the user's past feedback and propose an optimal reward management method. For example, the management unit proposes an optimal reward management method based on the user's feedback. In this way, the reward management method can be customized by reflecting the user's past feedback.

[0053] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0054] The acquisition unit can analyze the user's past location information history and predict the user's movement pattern. For example, the acquisition unit can predict the location the user is likely to visit next based on the locations the user has frequently visited in the past. The acquisition unit can also determine the optimal timing for acquiring location information based on the user's movement pattern. Furthermore, the acquisition unit can predict the locations the user will visit during a specific time period based on the user's past location information history. This makes it possible to predict the user's movement pattern and acquire optimal location information by analyzing the past location information history.

[0055] The providing unit can adjust the method of providing information based on the user's current activity status. For example, if the user is exercising, the providing unit can provide information by voice. If the user is reading, the providing unit can provide information by text. Furthermore, if the user is in a meeting, the providing unit can refrain from notifications. In this way, by adjusting the method of providing information according to the user's current activity status, it is possible to provide optimal information to the user.

[0056] The management unit can analyze the user's past behavioral history and predict the user's behavioral patterns. For example, the management unit can predict the next activity the user is likely to participate in based on the CO2 reduction activities the user has participated in in the past. The management unit can also determine the optimal timing for providing rewards based on the user's behavioral patterns. Furthermore, the management unit can predict the activities that will be performed during specific time periods based on the user's past behavioral history. This makes it possible to predict the user's behavioral patterns and provide optimal rewards by analyzing the user's past behavioral history.

[0057] The providing unit can adjust the method of providing information based on the user's current geographical location information. For example, if the user is in an urban area, the providing unit can provide detailed map information. If the user is in a natural environment, the providing unit can provide information on nature conservation activities. Furthermore, if the user is in a tourist destination, the providing unit can provide information on tourism-related CO2 reduction activities. In this way, by adjusting the method of providing information according to the user's current geographical location information, it is possible to provide optimal information to the user.

[0058] The management unit can analyze the user's social media activity and suggest types of rewards based on the user's areas of interest. For example, if the user posts many times on social media about environmental protection, the management unit can suggest rewards related to environmental protection. Also, if the user posts many times on social media about energy efficiency, the management unit can suggest rewards related to energy efficiency. Furthermore, if the user posts many times on social media about recycling, the management unit can suggest rewards related to recycling. In this way, by analyzing the user's social media activity, it is possible to suggest optimal rewards based on the user's areas of interest.

[0059] The processing flow of the first embodiment will be briefly explained below.

[0060] Step 1: The acquisition unit acquires location information of the user. For example, the acquisition unit can identify the user's current location using GPS data, Wi-Fi location information, and cell tower information. Step 2: The providing unit uses the generating AI to provide the latest information on CO2 reduction activities based on the location information acquired by the acquiring unit. For example, the providing unit causes the generating AI to refer to a database of CO2 reduction activities around the world and select the most appropriate information based on the user's location information. The generating AI can generate information using a machine learning model or a natural language generation model. Step 3: The management unit tracks the user's behavior and manages rewards based on the information provided by the provision unit. For example, when a user uses public transportation or an eco-bag, the management unit can use two-dimensional codes or GPS data to verify the user's behavior. The management unit can also accumulate points based on the amount of CO2 reduction the user contributes to and provide those points as rewards.

[0061] (Example 2) A system according to an embodiment of the present invention utilizes a generation AI to provide the latest information on global CO2 reduction activities in conjunction with a user's location information. This system acquires the user's location information, and the generation AI provides the latest information on CO2 reduction activities based on the acquired location information. The system also includes a mechanism for rewarding users based on their contributions to CO2 reduction. For example, the system acquires the user's location information and inputs it into the generation AI. The generation AI analyzes the input location information and provides the user with the latest information on CO2 reduction activities relevant to the user. For example, the system can provide information on tree planting activities and energy efficiency projects taking place in the user's area. Furthermore, the system also includes a mechanism for rewarding users based on their contributions to CO2 reduction. For example, users can earn points by using public transportation or eco-bags and receive rewards based on those points. These rewards can be provided in the form of electronic money or coupons. This encourages users to take voluntary action and raises awareness of addressing global warming issues. The system provides the latest information on CO2 reduction activities based on the user's location information, tracks the user's behavior, and manages rewards. For example, users can receive rewards while experiencing how their actions contribute to CO2 reduction.

[0062] A CO2 reduction activity information provision system according to an embodiment includes an acquisition unit, a provision unit, and a management unit. The acquisition unit acquires user location information. For example, the acquisition unit can identify the user's current location using GPS data. The acquisition unit can also identify the user's location using Wi-Fi location information. The acquisition unit can also identify the user's location using cell tower information. The provision unit uses a generation AI to provide the latest information on CO2 reduction activities based on the location information acquired by the acquisition unit. For example, the provision unit causes the generation AI to reference a database of CO2 reduction activities around the world and select optimal information based on the user's location information. The generation AI can generate information using, for example, a machine learning model or a natural language generation model. The management unit tracks user behavior and manages rewards based on the information provided by the provision unit. For example, the management unit can verify the user's actions when using public transportation or an eco-bag using a two-dimensional code (e.g., a QR code) or GPS data. The management unit can also accumulate points based on the user's contribution to CO2 reduction and provide the points as rewards. As a result, the CO2 reduction activity information providing system according to the embodiment can provide the latest information on CO2 reduction activities based on the user's location information, and can track the user's behavior to manage rewards.

[0063] The acquisition unit can identify the user's current location and input the location information to the generation AI. The acquisition unit can identify the user's current location using, for example, GPS data. For example, the acquisition unit can acquire location information using the GPS function of the user's smartphone. The acquisition unit can also identify the user's location using Wi-Fi location information. For example, the acquisition unit can collect information on surrounding Wi-Fi access points and identify the location based on that information. Furthermore, the acquisition unit can also identify the user's location using cell tower information. For example, the acquisition unit can identify the location based on information about the cell tower to which the user's smartphone is connected. In this way, the user's current location can be identified and input to the generation AI, thereby providing optimal information based on the location information.

[0064] The providing unit can use the generation AI to refer to a database of CO2 reduction activities around the world and select appropriate information based on the user's location information. For example, the generating AI can refer to a database of CO2 reduction activities around the world and select optimal information based on the user's location information. The generating AI can, for example, analyze the database using a machine learning model to extract relevant information. The generating AI can also generate information to be provided to the user using a natural language generation model. For example, the generating AI can provide information such as tree planting activities and energy efficiency projects being carried out in the user's area. The database includes, for example, types of CO2 reduction activities and activity information by region. This allows the generating AI to refer to the database and provide optimal information based on the user's location information.

[0065] The management unit can certify a user's actions when using public transportation or an eco-bag using two-dimensional codes or GPS data. For example, when a user uses public transportation, the management unit scans a two-dimensional code to certify the user's actions. For example, the management unit scans a two-dimensional code issued when boarding a bus or train and records the user's actions. The management unit can also certify a user's actions when using an eco-bag by scanning a two-dimensional code. For example, the management unit scans a two-dimensional code attached to the eco-bag and records the user's actions. Furthermore, the management unit can certify a user's actions using GPS data. For example, the management unit uses the GPS function of the user's smartphone to confirm that the user is in a specific location. This enables accurate tracking by certifying the user's actions using two-dimensional codes or GPS data.

[0066] The management unit can accumulate points based on the amount of CO2 reduction a user contributes to and provide the points as a reward. For example, the management unit can award points when a user uses public transportation. For example, the management unit can scan a two-dimensional code issued when boarding a bus or train and award the points. The management unit can also award points when a user uses an eco-bag. For example, the management unit can scan a two-dimensional code attached to the eco-bag and award the points. Furthermore, the management unit can award points for other actions that contribute to CO2 reduction by the user. For example, the management unit can award points when a user participates in recycling activities. Points are awarded based on the amount of CO2 reduction, for example. For example, the management unit can calculate and award points based on the amount of CO2 reduction a user has achieved. In this way, by awarding points for the user's CO2 reduction activities and providing them as a reward, it is possible to increase the user's motivation.

[0067] The provision unit can provide information on tree planting activities and energy efficiency projects being carried out in the user's area. For example, the generation AI provides information on tree planting activities being carried out in the user's area based on the user's location information. For example, the provision unit provides information on local tree planting events and volunteer activities. The provision unit can also provide information on energy efficiency projects being carried out in the user's area. For example, the provision unit provides information on improving local energy efficiency and introducing renewable energy. This makes it easier to attract the user's interest by providing information on CO2 reduction activities specific to the user's area.

[0068] The acquisition unit can estimate the user's emotion and adjust the timing of acquiring location information based on the estimated user emotion. For example, the acquisition unit captures the user's facial expression with a camera and estimates the emotion using an emotion estimation algorithm. For example, the acquisition unit calculates an emotion score based on changes in facial expression. The acquisition unit can also record the user's voice and estimate the emotion using voice analysis technology. For example, the acquisition unit analyzes the tone and speed of the voice and calculates the emotion score. Furthermore, the acquisition unit can collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotion using an emotion estimation algorithm. For example, the acquisition unit calculates the emotion score based on heart rate fluctuations. This allows the timing of acquiring location information to be adjusted according to the user's emotion, thereby reducing the burden on the user. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0069] The acquisition unit can analyze the user's past location information history and select an appropriate acquisition method. The acquisition unit, for example, adjusts the frequency of acquiring location information based on places the user has frequently visited in the past. For example, the acquisition unit analyzes the user's past location information history, and if the user frequently visits a particular place, increases the frequency of acquiring location information at that place. The acquisition unit can also analyze the user's movement pattern and acquire location information at the optimal timing. For example, the acquisition unit analyzes the user's movement pattern and acquires location information during times of heavy movement. Furthermore, the acquisition unit can predict places the user will visit during a particular time period and acquire location information during that time period. For example, the acquisition unit predicts places the user will visit during a particular time period based on the user's past location information history and acquires location information during that time period. In this way, the optimal location information acquisition method can be selected by analyzing the past location information history.

[0070] When acquiring location information, the acquisition unit can perform filtering based on the user's current activity status and areas of interest. For example, if the user is shopping, the acquisition unit prioritizes acquiring related CO2 reduction activity information. For example, the acquisition unit analyzes the user's current activity status, detects that the user is shopping, and acquires CO2 reduction activity information related to shopping. The acquisition unit can also prioritize acquiring information about energy efficiency if the user is playing sports. For example, the acquisition unit analyzes the user's current activity status, detects that the user is playing sports, and acquires information about energy efficiency. Furthermore, if the user is traveling, the acquisition unit can prioritize acquiring CO2 reduction activity information for the visited destination. For example, the acquisition unit analyzes the user's current activity status, detects that the user is traveling, and acquires CO2 reduction activity information for the visited destination. This allows more relevant information to be provided by filtering information based on the user's activity status and areas of interest.

[0071] When acquiring location information, the acquisition unit can select an appropriate acquisition means depending on the user's input method. For example, if the user is using voice input, the acquisition unit acquires location information using voice recognition technology. For example, the acquisition unit analyzes the user's voice input and identifies location information. Furthermore, if the user is using text input, the acquisition unit can also acquire location information using text analysis technology. For example, the acquisition unit analyzes the user's text input and identifies location information. Furthermore, if the user is using image input, the acquisition unit can also acquire location information using image analysis technology. For example, the acquisition unit analyzes an image taken by the user and identifies location information. This improves the accuracy of acquiring location information by selecting the optimal acquisition means depending on the user's input method.

[0072] The acquisition unit can estimate the user's emotions and determine the priority of location information to be acquired based on the estimated user emotions. For example, the acquisition unit captures the user's facial expression with a camera and estimates the emotion using an emotion estimation algorithm. For example, the acquisition unit calculates an emotion score based on changes in facial expression. The acquisition unit can also record the user's voice and estimate the emotion using voice analysis technology. For example, the acquisition unit analyzes the tone and speed of the voice and calculates the emotion score. Furthermore, the acquisition unit can collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotion using an emotion estimation algorithm. For example, the acquisition unit calculates the emotion score based on heart rate fluctuations. This allows the user to prioritize location information based on the user's emotions, thereby providing more appropriate information. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0073] When acquiring location information, the acquisition unit can prioritize acquiring highly relevant information by taking into account the user's geographical location information. For example, if the user is in an urban area, the acquisition unit prioritizes acquiring information about CO2 reduction activities being carried out in that city. For example, the acquisition unit analyzes the user's geographical location information and acquires information about CO2 reduction activities being carried out in the urban area. Furthermore, if the user is in a natural environment, the acquisition unit can also prioritize acquiring information about nature conservation activities. For example, the acquisition unit analyzes the user's geographical location information and acquires information about nature conservation activities being carried out in the natural environment. Furthermore, if the user is in a tourist destination, the acquisition unit can also prioritize acquiring information about tourism-related CO2 reduction activities in that area. For example, the acquisition unit analyzes the user's geographical location information and acquires information about CO2 reduction activities being carried out in the tourist destination. In this way, highly relevant information can be prioritized by taking into account the user's geographical location information.

[0074] When acquiring location information, the acquisition unit can analyze the user's social media activities and acquire related information. The acquisition unit, for example, acquires CO2 reduction activity information related to places where the user has checked in on social media. For example, the acquisition unit analyzes the user's social media activities and acquires information related to the checked-in places. The acquisition unit can also analyze the content of the user's posts on social media and acquire related CO2 reduction activity information. For example, the acquisition unit analyzes the content of the user's posts and acquires related information. Furthermore, the acquisition unit can also acquire related CO2 reduction activity information by referring to the activities of the user's friends on social media. For example, the acquisition unit analyzes the activities of the user's friends and acquires related information. In this way, related information can be acquired by analyzing the user's social media activities.

[0075] When acquiring location information, the acquisition unit can customize the acquisition method by reflecting the user's past feedback. The acquisition unit, for example, preferentially acquires location information from information sources that the user has previously rated highly. For example, the acquisition unit analyzes the user's past feedback and preferentially acquires location information from information sources that the user has previously rated highly. The acquisition unit can also adjust the acquisition method based on feedback provided by the user in the past. For example, the acquisition unit analyzes the user's feedback and customizes the acquisition method. Furthermore, the acquisition unit can analyze the user's past feedback and suggest an optimal acquisition method. For example, the acquisition unit suggests an optimal acquisition method based on the user's feedback. In this way, the acquisition method can be customized by reflecting the user's past feedback.

[0076] The providing unit can estimate the user's emotions and adjust the information provision method based on the estimated user's emotions. For example, the providing unit captures the user's facial expressions with a camera and estimates the emotions using an emotion estimation algorithm. For example, the providing unit calculates an emotion score based on changes in facial expressions. The providing unit can also record the user's voice and estimate the emotions using voice analysis technology. For example, the providing unit analyzes the tone and speed of the voice and calculates an emotion score. Furthermore, the providing unit can collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotions using an emotion estimation algorithm. For example, the providing unit calculates an emotion score based on heart rate fluctuations. This enables more effective information provision by adjusting the information provision method based on the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0077] The providing unit can adjust the level of detail of the information based on the importance of the CO2 reduction activity when providing the information. For example, the providing unit provides detailed information for CO2 reduction activities with high importance. For example, the providing unit provides detailed information about CO2 reduction activities with high importance. The providing unit can also provide concise information for CO2 reduction activities with low importance. For example, the providing unit provides concise information about CO2 reduction activities with low importance. Furthermore, the providing unit can gradually adjust the level of detail of the information according to the importance. For example, the providing unit adjusts the level of detail of the information according to the importance and provides it to the user. In this way, by adjusting the level of detail of the information based on the importance of the CO2 reduction activity, it is possible to provide useful information to the user.

[0078] When providing the information, the providing unit can apply different providing algorithms depending on the category of the CO2 reduction activity. For example, the providing unit provides information about tree planting activities using a visual map. For example, the providing unit displays information about tree planting activities using a visual map. The providing unit can also provide information about energy efficiency projects using a data chart. For example, the providing unit displays information about energy efficiency projects using a data chart. Furthermore, the providing unit can also provide information about recycling activities using a video. For example, the providing unit displays information about recycling activities using a video. This makes it possible to provide more appropriate information by adjusting the providing algorithm depending on the category of the CO2 reduction activity.

[0079] When providing information, the providing unit can improve the accuracy of the information provided by referring to the user's past information provision results. The providing unit, for example, preferentially provides information from information sources that the user has given a high rating in the past. For example, the providing unit analyzes the user's past information provision results and preferentially provides information from information sources that the user has given a high rating. The providing unit can also provide optimal information based on the user's past behavioral history. For example, the providing unit analyzes the user's past behavioral history and provides optimal information. Furthermore, the providing unit can analyze the user's past feedback and improve the accuracy of the information provided. For example, the providing unit improves the accuracy of the information provided by referring to the past information provision results.

[0080] The providing unit can estimate the user's emotion and adjust the length of the information based on the estimated user emotion. For example, the providing unit captures the user's facial expression with a camera and estimates the emotion using an emotion estimation algorithm. For example, the providing unit calculates an emotion score based on changes in facial expression. The providing unit can also record the user's voice and estimate the emotion using voice analysis technology. For example, the providing unit analyzes the tone and speed of the voice and calculates the emotion score. Furthermore, the providing unit can collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotion using an emotion estimation algorithm. For example, the providing unit calculates the emotion score based on heart rate fluctuations. This allows the length of information to be adjusted based on the user's emotion, thereby providing optimal information to the user. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0081] The providing unit can determine the priority of information at the time of providing the information based on the timing of CO2 reduction activities. For example, the providing unit prioritizes providing information about CO2 reduction activities that will be implemented most recently. For example, the providing unit prioritizes providing information about tree planting events or energy efficiency projects that will be implemented most recently. The providing unit can also provide information about long-term CO2 reduction projects in stages. For example, the providing unit provides progress reports on long-term energy efficiency projects in stages. Furthermore, the providing unit can adjust the priority of information depending on the timing of implementation. For example, the providing unit adjusts the priority of information depending on the season or the timing of an event and provides it to the user. This enables timely information provision by determining the priority of information based on the timing of CO2 reduction activities.

[0082] The providing unit can adjust the order of information based on the relevance of CO2 reduction activities when providing the information. For example, the providing unit first provides information that is most relevant to the user's current location. For example, the providing unit first provides CO2 reduction activity information related to the user's current location. The providing unit can also postpone less relevant information. For example, the providing unit postpones information that is less relevant to the user's current location. Furthermore, the providing unit can gradually adjust the order of information according to the relevance. For example, the providing unit adjusts the order of information based on the user's level of interest and past behavioral history and provides it to the user. In this way, by adjusting the order of information based on the relevance of CO2 reduction activities, the most relevant information to the user can be provided preferentially.

[0083] The providing unit can adjust the use of technical terms in the information according to the user's level of expertise when providing the information. For example, the providing unit provides information using detailed technical terms to a user with high expertise. For example, the providing unit provides detailed information using technical terms to a user with high expertise. The providing unit can also provide information using concise and easy-to-understand language to a user with low expertise. For example, the providing unit provides information using concise and easy-to-understand language to a user with low expertise. Furthermore, the providing unit can gradually adjust the use of technical terms in the information according to the user's level of expertise. For example, the providing unit adjusts the use of technical terms in the information according to the user's level of expertise and provides it to the user. In this way, by adjusting the use of technical terms in the information according to the user's level of expertise, it is possible to provide information that is easy for the user to understand.

[0084] The management unit can estimate the user's emotions and adjust the reward management method based on the estimated user emotions. For example, the management unit captures the user's facial expressions with a camera and estimates the emotions using an emotion estimation algorithm. For example, the management unit calculates an emotion score based on changes in facial expressions. The management unit can also record the user's voice and estimate the emotions using voice analysis technology. For example, the management unit analyzes the tone and speed of the voice and calculates an emotion score. Furthermore, the management unit can collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotions using an emotion estimation algorithm. For example, the management unit calculates an emotion score based on heart rate fluctuations. This enables optimal reward management for the user by adjusting the reward management method based on the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0085] During management, the management unit can analyze the user's past behavioral history and select the optimal reward management method. For example, the management unit preferentially selects reward management methods that the user has given high ratings to in the past. For example, the management unit analyzes the user's past behavioral history and selects highly rated reward management methods. The management unit can also select the optimal reward management method based on the user's past behavioral history. For example, the management unit analyzes the user's past behavioral history and selects the optimal reward management method. Furthermore, the management unit can analyze the user's past feedback and select the optimal reward management method. For example, the management unit selects the optimal reward management method based on the user's feedback. In this way, the optimal reward management method can be selected by analyzing the user's past behavioral history.

[0086] During management, the management unit can customize the reward management means based on the user's current living situation. For example, when the user is busy, the management unit provides a simple and quick reward management means. For example, the management unit provides a simple and quick reward management means when the user is busy. The management unit can also provide a detailed reward management means when the user is relaxed. For example, the management unit provides a detailed reward management means when the user is relaxed. Furthermore, the management unit can customize the reward management means according to the user's living situation. For example, the management unit analyzes the user's living situation and provides the optimal reward management means. In this way, by customizing the reward management means based on the user's living situation, optimal reward management for the user is possible.

[0087] During management, the management unit can improve the reward management method by reflecting user feedback. The management unit improves the reward management method, for example, based on feedback provided by the user in the past. For example, the management unit analyzes the user's feedback and improves the reward management method. The management unit can also analyze the user's feedback and propose an optimal reward management method. For example, the management unit proposes an optimal reward management method based on the user's feedback. Furthermore, the management unit can reflect the user's feedback and gradually improve the reward management method. For example, the management unit gradually improves the reward management method based on the user's feedback. In this way, the reward management method can be improved by reflecting the user's feedback.

[0088] The management unit can estimate the user's emotions and determine the priority of reward management based on the estimated user emotions. For example, the management unit captures the user's facial expressions with a camera and estimates the emotions using an emotion estimation algorithm. For example, the management unit calculates an emotion score based on changes in facial expressions. The management unit can also record the user's voice and estimate the emotions using voice analysis technology. For example, the management unit analyzes the tone and speed of the voice and calculates an emotion score. Furthermore, the management unit can collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotions using an emotion estimation algorithm. For example, the management unit calculates an emotion score based on heart rate fluctuations. This enables optimal reward management for the user by determining the priority of reward management based on the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0089] During management, the management unit can select an optimal reward management method by taking into account the user's geographical location information. For example, if the user is in an urban area, the management unit can prioritize providing rewards that can be used in the city. For example, the management unit can analyze the user's geographical location information and provide rewards that can be used in the urban area. Furthermore, if the user is in a natural environment, the management unit can prioritize providing rewards related to nature conservation activities. For example, the management unit can analyze the user's geographical location information and provide rewards that can be used in the natural environment. Furthermore, if the user is in a tourist destination, the management unit can prioritize providing rewards that can be used in that area. For example, the management unit can analyze the user's geographical location information and provide rewards that can be used in the tourist destination. In this way, the optimal reward management method can be selected by taking into account the user's geographical location information.

[0090] During management, the management unit can analyze the user's social media activity and suggest reward management measures. The management unit, for example, suggests rewards related to places the user has checked in to on social media. For example, the management unit analyzes the user's social media activity and suggests rewards related to places the user has checked in to. The management unit can also analyze the content of the user's posts on social media and suggest related rewards. For example, the management unit analyzes the content of the user's posts and suggests related rewards. Furthermore, the management unit can also suggest related rewards based on the activities of the user's friends on social media. For example, the management unit analyzes the activities of the user's friends and suggests related rewards. In this way, optimal reward management measures can be suggested by analyzing the user's social media activity.

[0091] During management, the management unit can customize the reward management method by reflecting the user's past feedback. For example, the management unit preferentially selects reward management methods that the user has given high ratings to in the past. For example, the management unit analyzes the user's past feedback and selects reward management methods that have received high ratings. The management unit can also adjust the reward management method based on the user's past feedback. For example, the management unit analyzes the user's feedback and customizes the reward management method. Furthermore, the management unit can analyze the user's past feedback and propose an optimal reward management method. For example, the management unit proposes an optimal reward management method based on the user's feedback. In this way, the reward management method can be customized by reflecting the user's past feedback. === Hard Collateral 1-1 === Each of the multiple elements including the acquisition unit, provision unit, and management 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 acquisition unit identifies the user's location using GPS data, Wi-Fi location information, and cell tower information from the smart device 14. The provision unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and provides the latest information on CO2 reduction activities based on the location information obtained from the acquisition unit using a generation AI. The management unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and tracks the user's behavior and manages rewards. === Hard Collateral 1-2 === Each of the multiple elements, including the acquisition unit, provision unit, and management 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 acquisition unit identifies the user's location using GPS data, Wi-Fi location information, and cell tower information from the smart glasses 214. The provision unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and provides the latest information on CO2 reduction activities based on the location information obtained from the acquisition unit using a generation AI. The management unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and tracks the user's behavior and manages rewards. === Hard Collateral 1-3 === Each of the multiple elements including the acquisition unit, provision unit, and management 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 acquisition unit identifies the user's location using GPS data, Wi-Fi location information, and cell tower information from the headset type terminal 314. The provision unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and provides the latest information on CO2 reduction activities based on the location information obtained from the acquisition unit using a generation AI. The management unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and tracks the user's behavior and manages rewards. === Hard Collateral 1-4 === Each of the multiple elements including the acquisition unit, provision unit, and management unit described above is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the acquisition unit identifies the user's location using GPS data, Wi-Fi location information, and cell tower information of the robot 414. The provision unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and provides the latest information on CO2 reduction activities based on the location information obtained from the acquisition unit using a generation AI. The management unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and tracks the user's behavior and manages rewards.

[0092] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0093] The providing unit can estimate the user's emotions and adjust the frequency of information provision based on the estimated user's emotions. For example, if the user is feeling stressed, the providing unit can reduce the frequency of information provision. Conversely, if the user is relaxed, the providing unit can increase the frequency of information provision. Furthermore, if the user is excited, the providing unit can provide important information preferentially. In this way, by adjusting the frequency of information provision according to the user's emotions, it is possible to provide optimal information to the user.

[0094] The management unit can estimate the user's emotions and adjust the type of reward based on the estimated user's emotions. For example, if the user is happy, the management unit can provide a special reward. If the user is sad, the management unit can provide a reward with an encouraging message. Furthermore, if the user is tired, the management unit can provide a reward that helps the user relax. In this way, adjusting the type of reward according to the user's emotions can increase user satisfaction.

[0095] The providing unit can estimate the user's emotions and adjust the display format of information based on the estimated user's emotions. For example, if the user is tired, the providing unit can provide concise visual information. If the user is excited, the providing unit can provide detailed text information. Furthermore, if the user is relaxed, the providing unit can provide information using video or audio. In this way, by adjusting the display format of information according to the user's emotions, it is possible to provide optimal information to the user.

[0096] The acquisition unit can estimate the user's emotions and adjust the accuracy of acquiring location information based on the estimated user's emotions. For example, if the user is feeling stressed, the acquisition unit can increase the accuracy of acquiring location information. Furthermore, if the user is relaxed, the acquisition unit can decrease the accuracy of acquiring location information. Furthermore, if the user is excited, the acquisition unit can increase the frequency of acquiring location information. In this way, by adjusting the accuracy of acquiring location information according to the user's emotions, it is possible to acquire location information that is optimal for the user.

[0097] The providing unit can estimate the user's emotions and adjust the timing of providing information based on the estimated user's emotions. For example, if the user is busy, the providing unit can delay the provision of information. Also, if the user is relaxed, the providing unit can speed up the provision of information. Furthermore, if the user is excited, the providing unit can provide important information immediately. In this way, by adjusting the timing of providing information according to the user's emotions, it is possible to provide optimal information to the user.

[0098] The acquisition unit can analyze the user's past location information history and predict the user's movement pattern. For example, the acquisition unit can predict the location the user is likely to visit next based on the locations the user has frequently visited in the past. The acquisition unit can also determine the optimal timing for acquiring location information based on the user's movement pattern. Furthermore, the acquisition unit can predict the locations the user will visit during a specific time period based on the user's past location information history. This makes it possible to predict the user's movement pattern and acquire optimal location information by analyzing the past location information history.

[0099] The providing unit can adjust the method of providing information based on the user's current activity status. For example, if the user is exercising, the providing unit can provide information by voice. If the user is reading, the providing unit can provide information by text. Furthermore, if the user is in a meeting, the providing unit can refrain from notifications. In this way, by adjusting the method of providing information according to the user's current activity status, it is possible to provide optimal information to the user.

[0100] The management unit can analyze the user's past behavioral history and predict the user's behavioral patterns. For example, the management unit can predict the next activity the user is likely to participate in based on the CO2 reduction activities the user has participated in in the past. The management unit can also determine the optimal timing for providing rewards based on the user's behavioral patterns. Furthermore, the management unit can predict the activities that will be performed during specific time periods based on the user's past behavioral history. This makes it possible to predict the user's behavioral patterns and provide optimal rewards by analyzing the user's past behavioral history.

[0101] The providing unit can adjust the method of providing information based on the user's current geographical location information. For example, if the user is in an urban area, the providing unit can provide detailed map information. If the user is in a natural environment, the providing unit can provide information on nature conservation activities. Furthermore, if the user is in a tourist destination, the providing unit can provide information on tourism-related CO2 reduction activities. In this way, by adjusting the method of providing information according to the user's current geographical location information, it is possible to provide optimal information to the user.

[0102] The management unit can analyze the user's social media activity and suggest types of rewards based on the user's areas of interest. For example, if the user posts many times on social media about environmental protection, the management unit can suggest rewards related to environmental protection. Also, if the user posts many times on social media about energy efficiency, the management unit can suggest rewards related to energy efficiency. Furthermore, if the user posts many times on social media about recycling, the management unit can suggest rewards related to recycling. In this way, by analyzing the user's social media activity, it is possible to suggest optimal rewards based on the user's areas of interest.

[0103] The processing flow of the second embodiment will be briefly explained below.

[0104] Step 1: The acquisition unit acquires location information of the user. For example, the acquisition unit can identify the user's current location using GPS data, Wi-Fi location information, and cell tower information. Step 2: The providing unit uses the generating AI to provide the latest information on CO2 reduction activities based on the location information acquired by the acquiring unit. For example, the providing unit causes the generating AI to refer to a database of CO2 reduction activities around the world and select the most appropriate information based on the user's location information. The generating AI can generate information using a machine learning model or a natural language generation model. Step 3: The management unit tracks the user's behavior and manages rewards based on the information provided by the provision unit. For example, when a user uses public transportation or an eco-bag, the management unit can use two-dimensional codes or GPS data to verify the user's behavior. The management unit can also accumulate points based on the amount of CO2 reduction the user contributes to and provide those points as rewards.

[0105] 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.

[0106] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0107] 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.

[0108] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0109] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0110] 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.

[0111] 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.

[0112] 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.

[0113] 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.

[0114] 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).

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0123] 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.

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

[0125] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0126] 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.

[0127] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0128] The 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.

[0129] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0130] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0131] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0132] Fig. 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.

[0133] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0134] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0135] In the 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.

[0136] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0137] The specific processing unit 290 transmits the result of the specific processing to the 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.

[0138] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0139] 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.

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

[0141] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0142] 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.

[0143] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0144] The 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.

[0145] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0146] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS 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).

[0147] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0156] 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.

[0157] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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).

[0162] 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.

[0163] 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."

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] [Explanation of symbols]

[0177] 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. an acquisition unit that acquires user location information; a providing unit that provides the latest information on CO2 reduction activities based on the location information acquired by the acquiring unit; a management unit that tracks user behavior and manages rewards based on the information provided by the provision unit. A system characterized by:

2. The acquisition unit Identify the user's current location and input that location information into the generation AI 2. The system of claim 1.

3. The providing unit Generative AI references a database of CO2 reduction activities around the world and selects appropriate information based on the user's location.

2. The system of claim 1.

4. The management unit When a user uses public transportation or an eco-bag, the user's actions are verified using a 2D code or GPS data.

2. The system of claim 1.

5. The management unit Users accumulate points based on their contribution to CO2 reduction, and these points are provided as rewards.

2. The system of claim 1.

6. The providing unit Provide users with information about tree planting and energy efficiency projects in their area 2. The system of claim 1.

7. The acquisition unit Estimates the user's emotions and adjusts the timing of acquiring location information based on the estimated user emotions.

2. The system of claim 1.

8. The acquisition unit Analyze the user's past location history and select the appropriate acquisition method 2. The system of claim 1.

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