system

Abstract

We provide the system. [Solution] A means of receiving environmental improvement action data from users, A means of analyzing received behavioral data and quantifying its impact on the environment, A method using a generation algorithm that simulates future environmental conditions based on the analysis results, A means of visually displaying the generated future environmental state, A means of providing feedback to the user based on the simulation results, A system that includes this.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a persona chatbot control method performed by at least one processor, the method including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a 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

Summary of the Invention

Problems to be Solved by the Invention

[0004] With the intensification of global warming and environmental problems, it is difficult to realize the impact of individual actions on the global environment, which has become a factor hindering the spread of positive eco-friendly actions. While many people recognize the importance of environmental protection, it is difficult to see how specific actions will contribute to the future, and there is a problem of insufficient motivation for spontaneous improvement actions.

Means for Solving the Problems

[0005] This invention provides a system comprising means for receiving environmental improvement behavior data from a user, means for analyzing the received behavior data and quantifying its impact on the environment, means for using a generation algorithm to simulate future environmental conditions based on the analysis results, means for visually displaying the generated future environmental conditions, and means for providing feedback to the user based on the simulation results. This makes it easier for users to visually understand how their eco-behaviors specifically affect the future environment, and motivates them to continue choosing sustainable behaviors. Furthermore, by enabling users to participate in eco-behavior challenges and compare their results with other users, it becomes possible to promote continuous eco-activities through the community.

[0006] A "user" refers to an individual or group that uses this system and provides data on environmental improvement actions.

[0007] "Environmental improvement behavior data" refers to information about environmentally conscious behaviors that users engage in in their daily lives, including, for example, their choice of transportation and actions to reduce energy consumption.

[0008] "Means of receiving data" refers to functions that reliably acquire environmental improvement action data sent by users and make it available for use within the system.

[0009] "Means of analysis" refers to a function that processes data received to calculate the specific numerical impact that user behavior has on the environment.

[0010] "Means of quantification" refers to functions that express environmental impacts using specific numerical values ​​and present them in a way that is easy for users to understand.

[0011] A "generative algorithm" refers to a computational method for predicting future environmental conditions and generating visual representations based on analyzed data.

[0012] "Means of simulation" refers to a function that uses generative algorithms to predict future environmental conditions and create concrete visual information based on those predictions.

[0013] "Means of visual representation" refers to screen display and image generation functions that present the generated future environmental state to the user in an easily understandable way.

[0014] "Means of providing feedback" refers to a function that, based on simulation results, informs users about the impact of environmental behavior and notifies them of the results in order to increase their motivation to act.

[0015] An "Eco Action Challenge" refers to a competitive activity in which multiple users participate with the aim of protecting the environment, and includes a mechanism for comparing results. [Brief explanation of the drawing]

[0016] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9]Shows an emotion map to which a plurality of emotions are mapped. [Figure 10] Shows an emotion map to which a plurality of emotions are mapped. [Figure 11] It is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] It is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] It is a sequence diagram showing the processing flow of the data processing system in Example 2 when the emotion engine is combined. [Figure 14] It is a sequence diagram showing the processing flow of the data processing system in Application Example 2 when the emotion engine is combined.

Modes for Carrying Out the Invention

[0017] Hereinafter, an example of an embodiment of a system according to the technology of the present disclosure will be described with reference to the accompanying drawings.

[0018] First, the terms used in the following description will be explained.

[0019] In the following embodiments, the numbered processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of a plurality of arithmetic units. Also, the processor may be a single type of arithmetic unit or a combination of a plurality of types of arithmetic units. Examples of arithmetic units 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), and the like.

[0020] In the following embodiments, signed RAM (Random Access Memory) is a memory that temporarily stores information and is used as work memory by the processor.

[0021] In the following embodiments, the signed storage is one or more non-volatile storage devices that store various programs and various parameters. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes.

[0022] In the following embodiments, the signed communication interface (I / F) is an interface that includes a communication processor and an antenna, etc. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0023] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." That is, "A and / or B" means that it may be A alone, or B alone, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" applies when expressing three or more things linked by "and / or."

[0024] [First Embodiment]

[0025] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0026] As shown in Figure 1, the 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.

[0027] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0028] The smart device 14 comprises a computer 36, a reception device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The reception device 38, output device 40, and camera 42 are also connected to the bus 52.

[0029] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, etc., and receives user input. The touch panel 38A receives user input by detecting contact with an object (e.g., a pen or finger). The microphone 38B receives user input by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0030] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form perceptible to the user 20 (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0031] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

[0032] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0033] As shown in Figure 2, in the data processing device 12, a specific processing 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" related to the technology of this 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 processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0034] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0035] In the smart device 14, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The reception output program 60 is used in conjunction with a specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0036] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0037] This invention is a system that provides visual feedback on the impact of environmentally conscious actions taken by users, based on their input. This system primarily uses a terminal, server, and generating AI to record and analyze the user's eco-friendly behavior and predict future environmental conditions.

[0038] Specifically, users first install a dedicated application and input their daily eco-activities, such as commuting by bicycle or recycling, into their device. This behavioral data is then sent from the device to a server. The server receives this data and uses an action analysis module to calculate the specific environmental effects of these eco-activities, such as the amount of carbon dioxide reduced.

[0039] Subsequently, the AI ​​generates simulations of future environmental conditions based on the analysis results. Specifically, it predicts changes in carbon dioxide concentration and average temperature if the user's actions continue for a certain period, and generates these changes in a visual format. The resulting simulation images are then sent to the device in a way that makes the effects easy to understand visually.

[0040] The device displays eco-points to the user along with a generated image of the future environmental state, thereby increasing motivation for action. It also provides an interface that allows users to participate in eco-action challenges and compare their results with those of other users. In this way, users can concretely understand how their actions contribute to the future and gain motivation to promote continuous eco-activities.

[0041] For example, if a user uses public transportation for their commute for a month, the system calculates the amount of carbon dioxide that can be reduced during that period and visualizes the global environmental changes that would occur if this continued for a year. This feedback helps users realize the impact their daily actions have on the planet and provides them with an opportunity to continue being more proactive in eco-friendly behavior.

[0042] The following describes the processing flow.

[0043] Step 1:

[0044] Users launch an application on their device and input their daily eco-friendly activities. For example, they record specific actions such as "I commuted by bicycle today" in an input form.

[0045] Step 2:

[0046] The terminal converts the entered behavioral data into the appropriate format. It verifies the data's integrity and prompts the user to correct the input if necessary. It then prepares the formatted data for transmission to the server.

[0047] Step 3:

[0048] The server receives data sent from the terminal. The data is then passed to the behavioral analysis module in real time. The received data is also stored in a database, making it available for future analysis and trend analysis.

[0049] Step 4:

[0050] The server uses a behavioral analysis module to quantify the specific environmental impact of the user's eco-friendly behavior based on the received data. For example, it calculates CO2 reduction amounts and the rate of reduction in electricity consumption.

[0051] Step 5:

[0052] Based on the analysis results, the server uses generated AI to perform future environmental simulations. The simulations predict long-term environmental changes if users continue their eco-friendly behaviors.

[0053] Step 6:

[0054] Images and graphics representing the generated future environmental state are sent from the server to the terminal. A visually easy-to-understand format is used, and measures are taken to provide clear feedback to the user.

[0055] Step 7:

[0056] The terminal displays simulation images received from the server to the user. It also displays the eco-points the user has earned, providing an incentive for their actions.

[0057] Step 8:

[0058] Users will use the displayed feedback to continue or improve their eco-friendly behaviors. In some cases, they will participate in challenges with other users and share their results to further promote eco-friendly activities.

[0059] (Example 1)

[0060] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0061] Currently, many individuals are taking action to contribute to environmental protection, but there is a problem in that it is difficult to recognize the specific extent of improvement that these actions are leading to. Furthermore, there is a lack of means to visualize the process by which individual activities accumulate and lead to global environmental improvement. For this reason, there is a need to build a system that provides quantitative and visual feedback on the impact of individual activities on the future environment.

[0062] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0063] In this invention, the server includes means for receiving activity information from users, means for analyzing the received activity information and quantifying its impact on the environment, and means for using a generative model to predict future environmental conditions based on the analysis results. This allows users to concretely understand the impact their activities will have on the future environment and receive feedback that enhances their motivation for eco-friendly activities.

[0064] A "user" is an individual or organization that uses the system to input activity information and receives environmental impact information.

[0065] "Activity information" refers to data about environmental protection activities carried out by users, and is information received by the system.

[0066] "Means of receiving" refers to the function that allows the server to retrieve activity information entered by the user.

[0067] "Means of analysis and quantification" refers to the process of expressing the impact on the environment as specific numerical values ​​based on the received activity information.

[0068] "Methods using generative models" refer to methods for simulating and predicting future environmental conditions using analyzed data.

[0069] "Means of visual output" refers to functions that represent predictive information obtained by generative models in a format that is easy for users to understand, such as graphs and images.

[0070] "Means of reporting" refers to the process of notifying and explaining to users the environmental impact of their actions based on visually generated predictive information.

[0071] To implement this invention, a terminal, a server, and a generative AI model are required.

[0072] Users first install a specific application software on their device and input information about their daily environmental protection activities. This app is designed to run on smartphones and tablets. By inputting data on specific environmental protection activities, such as the number of days they commuted by bicycle or the details of their recycling, eco-activity data is accumulated on their device. This input can be easily done by users using touch panels or voice recognition.

[0073] The terminal sends the input data to the server. The server records the received data using a database system and analyzes it using an activity analysis module. The activity analysis module includes algorithms built in specific programming languages ​​(such as Python or Java®) that quantitatively evaluate the environmental impact (e.g., carbon dioxide reduction) based on the input activity information.

[0074] Next, the server uses a generative AI model to simulate future environmental conditions. This model is developed using machine learning frameworks (such as Tensorflow® and PyTorch). It receives analyzed data as input and predicts how the user's continued eco-friendly activities will impact the global economy. The server provides the generative AI with prompts such as, "Visualize the carbon dioxide reduction effect if the user commutes by bicycle for one year."

[0075] The simulation results are generated as visual content. For example, graphs showing the predicted decrease in carbon dioxide concentration and images showing the trend of future average temperature fluctuations are generated. This allows users to intuitively understand the impact their activities will have on the future environment.

[0076] The device displays eco-points to the user for their activities, along with visual feedback. This provides a mechanism to maintain and improve user motivation, and also includes an interface for comparing results with other users. This is achieved by utilizing integration with social platforms and other similar systems.

[0077] This system configuration makes it possible to accurately understand how individual eco-activities contribute to the future environment, and to provide ecological action support that is easy for users to understand and that increases their motivation to participate.

[0078] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0079] Step 1:

[0080] Users install a dedicated application on their device and input information about their daily environmental protection activities. For example, users enter data such as "I commuted by bicycle today" into an input form. The entered information is temporarily stored in a database on the device. At this stage, the input data is retained in its original format.

[0081] Step 2:

[0082] The device transmits the entered activity information to the server via the internet. Encryption is applied to protect the information during this transmission. The server receives this data and records it in a database. The received data is stored as structured data, including elements such as activity type, date, and frequency.

[0083] Step 3:

[0084] The server passes the received data to the analysis module. The analysis module uses an algorithm to quantify the environmental impact (e.g., carbon dioxide reduction) based on the input activity information. In this process, specific reduction amounts are calculated using historical trend data and environmental coefficients. The output provides the reduction amount and the quantified effect of eco-activities.

[0085] Step 4:

[0086] The server uses a generative AI model to simulate future environmental conditions based on the analysis results. The server sends prompts to the generative AI, such as "Visualize the carbon dioxide reduction effect if a user commutes by bicycle for one year," and initiates the simulation. The generative AI model receives the analysis results as input, makes predictions about future trends, and expresses them as visual content. The output here includes graphs and images showing the predicted environmental conditions.

[0087] Step 5:

[0088] The device displays visual feedback and eco-point information sent back from the server to the user. The screen shows a graph of predicted future environmental changes along with a message such as, "You have earned XX points for your activities." This allows users to intuitively understand how their actions have a concrete environmental impact. This feedback serves to motivate users to continue their environmental activities.

[0089] (Application Example 1)

[0090] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0091] Building a sustainable society requires individuals to recognize the impact their daily actions have on the environment and to encourage more environmentally conscious choices. However, consumers lack visible means of understanding the environmental impact of products, which makes it difficult to translate this into concrete behavioral changes.

[0092] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0093] In this invention, the server includes means for receiving environmental improvement action data from the user, means for acquiring product identification information and acquiring environmental impact information from a product characteristics database, and means for providing the user with environmental impact information and providing feedback on the eco-footprint. This makes it possible for consumers to immediately understand the environmental impact of the products they purchase and to visually grasp the impact their choices will have on the future environment.

[0094] A "user" is a person who uses this system to record and analyze environmental improvement actions.

[0095] "Environmental improvement behavior data" refers to information about environmentally conscious behaviors provided by users.

[0096] A "generative algorithm" is a computational procedure used to simulate future environmental conditions based on received data.

[0097] "Product identification information" refers to data codes or barcode information used to identify a specific product.

[0098] A "product characteristics database" is a collection of data that records environmental impact information and other data related to each product.

[0099] "Environmental impact information" refers to numerical data about the impact that a product has on the environment during its manufacturing, distribution, and consumption processes.

[0100] "Eco-footprint" is a numerical or visual indicator that represents the impact of a user's consumption behavior on the environment.

[0101] To realize this invention, a server system operating on the cloud and a user-operable terminal device are required. The server receives environmental improvement action data from the user, analyzes the data, and quantifies its impact on the environment. The quantified data is used to simulate future environmental conditions using a generation algorithm. In this process, a generation AI model is utilized to perform highly accurate predictions.

[0102] When a user provides product identification information through their device, the server retrieves relevant environmental impact information from a product characteristics database. This allows for the calculation of a specific eco-footprint, which is then fed back to the user. The device is equipped with a camera for identification (e.g., a smartphone's camera API) and a display for displaying visual information (smart glasses or a display unit).

[0103] As a concrete example, when a user purchases organic food at a supermarket, they scan the product's barcode using their smartphone camera. Based on this information, the server retrieves the product's eco-footprint information from a product characteristics database and visualizes the calculated environmental impact, displaying it on the user's device. This feedback allows the user to instantly understand the environmental impact of their purchase.

[0104] The generative AI model operates based on the prompt statement, "When a user selects a specific product, the AI ​​will visualize and present the environmental impact of that product based on past data."

[0105] The hardware used includes smartphones and smart glasses, while the software includes data processing modules using Python and generative AI models using TensorFlow and PyTorch. By combining these technologies, a system will be built that enables visual feedback on consumer behavior and environmental impact.

[0106] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0107] Step 1:

[0108] The user scans the product's barcode using the device's camera. The input in this step is the product's barcode information, and the device converts this input data into a digital code and sends it to the server.

[0109] Step 2:

[0110] The server queries a product characteristics database based on the received barcode information to obtain environmental impact information for the relevant product. The input is barcode information, and the output is environmental impact information obtained from the product characteristics database. This database matching provides specific environmental impact indicators related to the product.

[0111] Step 3:

[0112] The server analyzes the environmental impact information it has acquired and uses a generated AI model to simulate future environmental impacts. In this step, the AI ​​model uses the input environmental impact information to generate predictive images of future environmental changes, following the prompt message: "When a user selects a specific product, visualize and present the impact that product will have on the environment, based on past data."

[0113] Step 4:

[0114] The server sends the environmental simulation results it generates to the terminal. The input here is the simulation result, and the output is image data formatted in a visually easy-to-understand format. The terminal receives this data and prepares to provide feedback to the user in the next phase.

[0115] Step 5:

[0116] The device displays visual feedback on environmental impact information to the user. The input is visual data sent from the server, and the output is environmental impact visualization information displayed on the screen. This allows the user to visually understand the potential environmental impact of their consumption behavior in the future.

[0117] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0118] This invention is an eco-activity system that records users' environmental improvement actions, provides more effective feedback using an emotion engine, and promotes user behavior. In this system, users input their daily eco-activities using a mobile device or computer terminal, and this data is transmitted to a server.

[0119] The server analyzes the received behavioral data and quantifies the specific environmental impact. This includes, for example, CO2 reduction and energy consumption. Next, the generating AI module simulates future environmental conditions based on the analysis results. This shows the long-term environmental impact predicted if the user's eco-friendly behavior continues. The generated future environmental conditions are sent from the server to the user's terminal in a visual format.

[0120] The addition of an emotion engine enables even richer feedback. The emotion engine recognizes the user's emotional state and customizes the feedback accordingly. For example, if the user is experiencing positive emotions, it provides specific advice or invitations to challenges to encourage further eco-friendly behavior. On the other hand, if negative emotions are detected, it lowers the barriers to suggested actions or sends messages reaffirming the importance of eco-friendly behavior, supporting the user in developing a more positive mindset.

[0121] For example, if a user enters "I used my bicycle when I went out today," the server analyzes the environmental impact of this action, and the emotion engine provides feedback considering the user's emotions at that time. If the system determines that the user has achieved something, a message such as "Keep it up! You can expect to reduce CO2 emissions by another 100 kg" will be displayed. Conversely, if the system determines that the user's motivation is low, an encouraging message such as "Even small actions add up. You took a great first step today" will be presented.

[0122] This system allows users to not only receive notifications about their eco-achievement levels, but also personalized feedback tailored to their emotions at the time, making it easier to maintain motivation. This is expected to promote sustainable environmental improvement actions and foster deeper community engagement.

[0123] The following describes the processing flow.

[0124] Step 1:

[0125] The user launches an application on their device and enters details of their eco-friendly actions for the day, such as "I went shopping by bicycle."

[0126] Step 2:

[0127] The terminal formats the entered eco-behavior data and verifies its integrity. If there are no problems, it prepares to send the data to the server.

[0128] Step 3:

[0129] The server receives eco-behavior data transmitted from the device. The received data is stored in a database and passed to the analysis module in real time.

[0130] Step 4:

[0131] The server uses a behavioral analysis module to quantify the specific environmental impact of users' eco-friendly behaviors, such as the amount of CO2 reduction.

[0132] Step 5:

[0133] After the analysis results are obtained, the server uses a generation AI module to simulate future environmental conditions. It generates predictions of environmental changes if the user continues to take action.

[0134] Step 6:

[0135] The generated environmental simulation images are sent from the server to the terminal. This includes visual feedback that visualizes the long-term impact of the user's eco-friendly behavior.

[0136] Step 7:

[0137] The terminal displays the received simulation image to the user. Simultaneously, it activates the emotion engine to provide feedback to the user.

[0138] Step 8:

[0139] The emotion engine analyzes the user's emotional state through the device. Based on the user's input and actions, it determines the emotional tone.

[0140] Step 9:

[0141] The server receives information from the emotion engine and generates feedback messages appropriate to the user's emotional state.

[0142] Step 10:

[0143] The feedback messages sent from the server to the terminal will be tailored to the user's emotions and designed to motivate them to engage in eco-friendly behavior.

[0144] Step 11:

[0145] Users can view emotion-based feedback on their devices and plan their future eco-friendly actions. They can also interact with other users through community features.

[0146] (Example 2)

[0147] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0148] Conventional environmental improvement support systems have struggled to provide sufficient motivation for users to voluntarily continue eco-friendly behavior, and their feedback is not tailored to individual circumstances or emotions, limiting their ability to encourage sustained user action. Furthermore, they have problems with insufficient quantification and visualization of environmental impact, making it difficult for users to understand the concrete effects of their actions.

[0149] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0150] In this invention, the server includes means for receiving environmental improvement behavior information from the user, means for analyzing the received behavior information and quantifying its impact on the environment, and means for using a generation method to simulate future environmental conditions based on the analysis results. This allows the user to accurately understand the specific impact their actions have on the environment, and by providing feedback on this information according to their individual emotional state, they can continue to improve their behavior more effectively.

[0151] A "user" is an individual who uses this system to record their own environmental improvement actions and receive feedback.

[0152] "Environmental improvement activity information" refers to data on eco-friendly activities that users engage in in their daily lives.

[0153] "Means" refers to the apparatus or method used to achieve a specific objective.

[0154] "Methods for analyzing behavioral information and quantifying its impact on the environment" refers to the process of converting user-inputted behavioral information into numerical values ​​and expressing the impact of those actions on the environment as concrete numerical data.

[0155] "Methods using generational techniques" refers to the process of estimating future environmental conditions using predictive algorithms and simulation techniques.

[0156] "Means of visual conversion and output" refers to technologies that convert numerical data and simulation results into graphs and charts that are easy for users to understand and display.

[0157] "Means of recognizing emotional states and adjusting feedback accordingly" refers to a process that analyzes the user's current emotions and provides optimal messages and advice based on those results.

[0158] "Means of providing information analysis and feedback to users through a secure channel" refers to the process of transmitting analysis results and feedback to users using a secure communication protocol.

[0159] This invention is a system designed to promote environmentally friendly behavior, supporting sustainable eco-activities by analyzing user behavior data and providing personalized feedback.

[0160] Users input their daily eco-friendly activities using mobile or computer devices. This input includes text data such as, for example, "I used my bicycle when I went out today." The device sends the input data to a server. This communication typically uses an internet connection.

[0161] The server uses data analysis libraries (e.g., Pandas, NumPy) to analyze the received behavioral data. This analysis quantifies the impact of the user's eco-friendly behavior on the environment. For example, it may be expressed as a reduction in CO2 emissions or energy consumption.

[0162] Next, the server uses a generative AI model to simulate future environmental states based on user behavior. This process employs machine learning frameworks (e.g., TensorFlow, PyTorch), and the simulated data is transformed into a visual format. This allows users to visually understand how their actions will affect the environment in the future.

[0163] Furthermore, the server is equipped with an emotion engine that uses natural language processing technology to infer the user's emotional state. Based on the user's text input and past behavioral data, it recognizes the user's current emotions and generates corresponding feedback messages. If the user is experiencing positive emotions, a message encouraging more challenging eco-friendly behaviors is generated. On the other hand, if negative emotions are detected, a message emphasizing encouragement and the importance of action is generated.

[0164] Examples of prompts include: "Suggest a feedback message to send when the user is feeling positive emotions," and "Generate specific advice to help the user continue eco-friendly behavior."

[0165] In this way, users can understand the specific impact of their actions and receive personalized feedback, which is expected to promote sustainable eco-friendly activities.

[0166] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0167] Step 1:

[0168] Users input their environmental improvement actions using mobile or computer terminals. Specifically, they input their eco-friendly activities (e.g., "I commuted by bicycle") in text format into the terminal. The terminal checks the integrity of this input data, converts it to the correct format, and then sends it to the server. The input data contains information about observed eco-friendly activities, and the output is a notification that the transmission to the server is complete.

[0169] Step 2:

[0170] The server receives behavioral data sent from the terminal. The received data is stored in a database and then processed using a data analysis library (e.g., Pandas). The server then analyzes the content of the behavior and begins calculations to quantify its impact on the environment. The input is the user's eco-behavior information, and the output is a numerical representation of the impact (e.g., CO2 reduction).

[0171] Step 3:

[0172] The server uses a generative AI model based on the quantified impact results to simulate the predicted future environmental state if the user's eco-friendly behavior continues. This generative process utilizes machine learning algorithms (e.g., TensorFlow) to make long-term predictions. The input is the quantified impact results, and the output is numerical data of the predicted environmental state.

[0173] Step 4:

[0174] The server visualizes the simulation results. It converts the generated numerical data into graphs and charts using a graphics library, making it easy for the user to understand. The input is numerical data of the predicted environmental state, and the output is visualized environmental prediction information.

[0175] Step 5:

[0176] The server analyzes the user's emotional state using an emotion engine and generates appropriate feedback. It analyzes text data using natural language processing techniques to recognize the user's current emotions. Inputs include past behavioral data and user text comments, while output is an emotion-based feedback message.

[0177] Step 6:

[0178] The server sends generated feedback messages and visualized environmental forecast information to the terminal via a secure protocol. Users receive this feedback and information on their terminal and use it to understand the results of their eco-friendly actions and to motivate themselves for the next steps. The input is the feedback message and visualized information, and the output is the display on the user's terminal.

[0179] (Application Example 2)

[0180] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0181] There is a need for a feedback system that streamlines users' environmental improvement actions and promotes sustainable eco-activities. This invention aims to maintain user motivation and promote sustainable behavioral change by recognizing the user's emotional state and providing customized feedback accordingly.

[0182] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0183] In this invention, the server includes means for receiving environmental improvement behavior data from the user, means for analyzing the received behavior data and quantifying its impact on the environment, and means for using a recognition engine to identify the emotional state and adjust the feedback according to that emotion. This makes it possible to customize the feedback based on the user's emotional state.

[0184] "Environmental improvement behavior data" refers to information about environmentally friendly actions taken by users, such as records of actions aimed at saving energy or reducing carbon dioxide emissions.

[0185] "Quantification" refers to the process of converting analyzed data into specific numerical values, making it possible to evaluate it quantitatively.

[0186] A "generative algorithm" is a computational procedure or method used to predict future environmental conditions based on the results of data analysis.

[0187] "Visually displaying" means providing generated information in a way that is easy for the user to understand, using a visual medium such as a display.

[0188] "Feedback" refers to evaluations and advice given to users regarding their actions, serving as motivation to promote or improve their future behavior.

[0189] "Emotional state" refers to the mental state or mood a user is experiencing at a particular moment, and it is a factor that influences their behavior.

[0190] A "recognition engine" refers to technology used to analyze a user's emotions and behavior, and is a mechanism for determining the emotions a user is experiencing.

[0191] This system works by collecting environmental improvement behavior data using users' smart devices and computers and sending it to the cloud. When users input their daily eco-friendly actions, this data is sent to the server. The server analyzes this data and quantifies its environmental impact. This quantification includes CO2 reduction and energy consumption.

[0192] Based on the analysis results, the server simulates future environmental conditions using a generated AI model. The generated future environmental conditions are sent to the user's device as visual content. This content is displayed on the user's smart device or computer display in an intuitively easy-to-understand format.

[0193] Furthermore, this system uses an emotion recognition engine to identify the user's emotional state. This is done through facial expression and voice analysis. Based on this emotional state, the system fine-tunes the feedback. If positive emotions are identified, it encourages more challenging eco-activities; if negative emotions are identified, it suggests more realistic and achievable activities. This strengthens the user's motivation to consistently engage in environmental improvement behaviors.

[0194] For example, if a user enters "I commuted by train today," the server analyzes the environmental impact of this action, and if the emotion recognition engine determines that the user has achieved something, it provides a message such as, "Your train commute reduced CO2 emissions by 20 kg. That's great! If you keep it up, you can reach 100 kg per month!" In this example, the feedback is appropriately adjusted to maintain the user's motivation.

[0195] An example of a prompt message would be: "Visually represent the CO2 reduction effect of a user choosing the train as an eco-friendly activity in 100 characters or less. Also, add an encouraging message if you determine that the user has achieved a sense of accomplishment." This is how the input is given to the generating AI model.

[0196] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0197] Step 1:

[0198] Users input data on their daily environmental improvement actions using a smart device or computer. The input data includes information about specific actions (e.g., cycling to work, using reusable items) and is sent to a server.

[0199] Step 2:

[0200] The server analyzes the received behavioral data and quantifies its environmental impact. This quantification includes CO2 reduction and energy consumption reduction effects. Using the received behavioral data as input, a specific transformation algorithm is applied to output concrete environmental indicators. This process measures the extent to which user behavior contributes to the environment.

[0201] Step 3:

[0202] The server uses a generative AI model to simulate future environmental conditions based on the analysis results. It takes quantified environmental impact data as input, the AI ​​performs the simulation, and outputs environmental visual data, including future predictions. This visualizes the long-term contribution to the environment.

[0203] Step 4:

[0204] The server uses an emotion recognition engine to identify the user's emotional state. It takes facial expression data and voice data acquired from the device's camera and microphone as input, analyzes it, and outputs the user's emotional state. This reveals what emotions the user is currently experiencing.

[0205] Step 5:

[0206] The server generates feedback based on simulation results and the user's emotional state. Using simulation results and emotional data as input, it generates prompts and outputs customized feedback messages using a generation AI model. The system operates by adjusting the feedback accurately to match the user's emotions.

[0207] Step 6:

[0208] The terminal displays feedback messages and visual content generated by the server to the user. It takes data received from the server as input and outputs it to the screen in a visually easy-to-understand format. This allows the user to visually confirm their environmental improvement actions and motivates them to take further action.

[0209] 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating 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.

[0210] Data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0211] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart device 14.

[0212] [Second Embodiment]

[0213] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0214] As shown in Figure 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.

[0215] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0216] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0217] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0218] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0219] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0220] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0221] The specific processing program 56 is an example of a "program" relating to the technology of this 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 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.

[0222] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0223] In the smart glasses 214, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0224] Next, the identification processing performed by the identification processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0225] This invention is a system that provides visual feedback on the impact of environmentally conscious actions taken by users, based on their input. This system primarily uses a terminal, server, and generating AI to record and analyze the user's eco-friendly behavior and predict future environmental conditions.

[0226] Specifically, users first install a dedicated application and input their daily eco-activities, such as commuting by bicycle or recycling, into their device. This behavioral data is then sent from the device to a server. The server receives this data and uses an action analysis module to calculate the specific environmental effects of these eco-activities, such as the amount of carbon dioxide reduced.

[0227] Subsequently, the AI ​​generates simulations of future environmental conditions based on the analysis results. Specifically, it predicts changes in carbon dioxide concentration and average temperature if the user's actions continue for a certain period, and generates these changes in a visual format. The resulting simulation images are then sent to the device in a way that makes the effects easy to understand visually.

[0228] The device displays eco-points to the user along with a generated image of the future environmental state, thereby increasing motivation for action. It also provides an interface that allows users to participate in eco-action challenges and compare their results with those of other users. In this way, users can concretely understand how their actions contribute to the future and gain motivation to promote continuous eco-activities.

[0229] For example, if a user uses public transportation for their commute for a month, the system calculates the amount of carbon dioxide that can be reduced during that period and visualizes the global environmental changes that would occur if this continued for a year. This feedback helps users realize the impact their daily actions have on the planet and provides them with an opportunity to continue being more proactive in eco-friendly behavior.

[0230] The following describes the processing flow.

[0231] Step 1:

[0232] Users launch an application on their device and input their daily eco-friendly activities. For example, they record specific actions such as "I commuted by bicycle today" in an input form.

[0233] Step 2:

[0234] The terminal converts the entered behavioral data into the appropriate format. It verifies the data's integrity and prompts the user to correct the input if necessary. It then prepares the formatted data for transmission to the server.

[0235] Step 3:

[0236] The server receives data sent from the terminal. The data is then passed to the behavioral analysis module in real time. The received data is also stored in a database, making it available for future analysis and trend analysis.

[0237] Step 4:

[0238] The server uses a behavioral analysis module to quantify the specific environmental impact of the user's eco-friendly behavior based on the received data. For example, it calculates CO2 reduction amounts and the rate of reduction in electricity consumption.

[0239] Step 5:

[0240] Based on the analysis results, the server uses generated AI to perform future environmental simulations. The simulations predict long-term environmental changes if users continue their eco-friendly behaviors.

[0241] Step 6:

[0242] Images and graphics representing the generated future environmental state are sent from the server to the terminal. A visually easy-to-understand format is used, and measures are taken to provide clear feedback to the user.

[0243] Step 7:

[0244] The terminal displays simulation images received from the server to the user. It also displays the eco-points the user has earned, providing an incentive for their actions.

[0245] Step 8:

[0246] Users will use the displayed feedback to continue or improve their eco-friendly behaviors. In some cases, they will participate in challenges with other users and share their results to further promote eco-friendly activities.

[0247] (Example 1)

[0248] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0249] Currently, many individuals are taking action to contribute to environmental protection, but there is a problem in that it is difficult to recognize the specific extent of improvement that these actions are leading to. Furthermore, there is a lack of means to visualize the process by which individual activities accumulate and lead to global environmental improvement. For this reason, there is a need to build a system that provides quantitative and visual feedback on the impact of individual activities on the future environment.

[0250] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0251] In this invention, the server includes means for receiving activity information from users, means for analyzing the received activity information and quantifying its impact on the environment, and means for using a generative model to predict future environmental conditions based on the analysis results. This allows users to concretely understand the impact their activities will have on the future environment and receive feedback that enhances their motivation for eco-friendly activities.

[0252] A "user" is an individual or organization that uses the system to input activity information and receives environmental impact information.

[0253] "Activity information" refers to data about environmental protection activities carried out by users, and is information received by the system.

[0254] "Means of receiving" refers to the function that allows the server to retrieve activity information entered by the user.

[0255] "Means of analysis and quantification" refers to the process of expressing the impact on the environment as specific numerical values ​​based on the received activity information.

[0256] "Methods using generative models" refer to methods for simulating and predicting future environmental conditions using analyzed data.

[0257] "Means of visual output" refers to functions that represent predictive information obtained by generative models in a format that is easy for users to understand, such as graphs and images.

[0258] "Means of reporting" refers to the process of notifying and explaining to users the environmental impact of their actions based on visually generated predictive information.

[0259] To implement this invention, a terminal, a server, and a generative AI model are required.

[0260] Users first install a specific application software on their device and input information about their daily environmental protection activities. This app is designed to run on smartphones and tablets. By inputting data on specific environmental protection activities, such as the number of days they commuted by bicycle or the details of their recycling, eco-activity data is accumulated on their device. This input can be easily done by users using touch panels or voice recognition.

[0261] The terminal sends the input data to the server. The server records the received data using a database system and analyzes it using an activity analysis module. The activity analysis module includes algorithms built in specific programming languages ​​(such as Python or Java) that quantitatively evaluate the environmental impact (e.g., carbon dioxide reduction) based on the input activity information.

[0262] Next, the server uses a generative AI model to simulate future environmental conditions. This model is developed using machine learning frameworks (such as TensorFlow and PyTorch). It receives the analyzed data as input and predicts how the user's continued eco-friendly activities will impact the global economy. The server provides the generative AI with prompts such as, "Visualize the carbon dioxide reduction effect if the user commutes by bicycle for one year."

[0263] The simulation results are generated as visual content. For example, graphs showing the predicted decrease in carbon dioxide concentration and images showing the trend of future average temperature fluctuations are generated. This allows users to intuitively understand the impact their activities will have on the future environment.

[0264] The device displays eco-points to the user for their activities, along with visual feedback. This provides a mechanism to maintain and improve user motivation, and also includes an interface for comparing results with other users. This is achieved by utilizing integration with social platforms and other similar systems.

[0265] This system configuration makes it possible to accurately understand how individual eco-activities contribute to the future environment, and to provide ecological action support that is easy for users to understand and that increases their motivation to participate.

[0266] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0267] Step 1:

[0268] Users install a dedicated application on their device and input information about their daily environmental protection activities. For example, users enter data such as "I commuted by bicycle today" into an input form. The entered information is temporarily stored in a database on the device. At this stage, the input data is retained in its original format.

[0269] Step 2:

[0270] The device transmits the entered activity information to the server via the internet. Encryption is applied to protect the information during this transmission. The server receives this data and records it in a database. The received data is stored as structured data, including elements such as activity type, date, and frequency.

[0271] Step 3:

[0272] The server passes the received data to the analysis module. The analysis module uses an algorithm to quantify the environmental impact (e.g., carbon dioxide reduction) based on the input activity information. In this process, specific reduction amounts are calculated using historical trend data and environmental coefficients. The output provides the reduction amount and the quantified effect of eco-activities.

[0273] Step 4:

[0274] The server uses a generative AI model to simulate future environmental conditions based on the analysis results. The server sends prompts to the generative AI, such as "Visualize the carbon dioxide reduction effect if a user commutes by bicycle for one year," and initiates the simulation. The generative AI model receives the analysis results as input, makes predictions about future trends, and expresses them as visual content. The output here includes graphs and images showing the predicted environmental conditions.

[0275] Step 5:

[0276] The device displays visual feedback and eco-point information sent back from the server to the user. The screen shows a graph of predicted future environmental changes along with a message such as, "You have earned XX points for your activities." This allows users to intuitively understand how their actions have a concrete environmental impact. This feedback serves to motivate users to continue their environmental activities.

[0277] (Application Example 1)

[0278] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0279] Building a sustainable society requires individuals to recognize the impact their daily actions have on the environment and to encourage more environmentally conscious choices. However, consumers lack visible means of understanding the environmental impact of products, which makes it difficult to translate this into concrete behavioral changes.

[0280] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0281] In this invention, the server includes means for receiving environmental improvement action data from the user, means for acquiring product identification information and acquiring environmental impact information from a product characteristics database, and means for providing the user with environmental impact information and providing feedback on the eco-footprint. This makes it possible for consumers to immediately understand the environmental impact of the products they purchase and to visually grasp the impact their choices will have on the future environment.

[0282] A "user" is a person who uses this system to record and analyze environmental improvement actions.

[0283] "Environmental improvement behavior data" refers to information about environmentally conscious behaviors provided by users.

[0284] The "generation algorithm" is a computational procedure for simulating future environmental conditions based on the received data.

[0285] The "product identification information" is a data code or barcode information used to identify a specific product.

[0286] The "product characteristic database" is an aggregation of data recording environmental load information and the like for each product.

[0287] The "environmental load information" is numerical data regarding the impact on the environment during the process of a product from manufacturing, distribution to consumption.

[0288] The "eco footprint" is a numerical or visual indicator representing the impact of a user's consumption behavior on the environment.

[0289] To implement this invention, a server system operating on the cloud and a terminal device operable by a user are required. The server receives environmental improvement action data from the user, analyzes the data, and quantifies the impact on the environment. The quantified data simulates future environmental conditions using the generation algorithm. In so doing, a high-precision prediction is carried out by leveraging the generation AI model.

[0290] When the user provides product identification information through the terminal, the server acquires relevant environmental load information from the product characteristic database. Thereby, a specific eco footprint is calculated and fed back to the user. The terminal is equipped with a camera for identification (e.g., the camera API of a smartphone) and a display (smart glasses or a display unit) for displaying visual information.

[0291] As a concrete example, when a user purchases organic food at a supermarket, they scan the product's barcode using their smartphone camera. Based on this information, the server retrieves the product's eco-footprint information from a product characteristics database and visualizes the calculated environmental impact, displaying it on the user's device. This feedback allows the user to instantly understand the environmental impact of their purchase.

[0292] The generative AI model operates based on the prompt statement, "When a user selects a specific product, the AI ​​will visualize and present the environmental impact of that product based on past data."

[0293] The hardware used includes smartphones and smart glasses, while the software includes data processing modules using Python and generative AI models using TensorFlow and PyTorch. By combining these technologies, a system will be built that enables visual feedback on consumer behavior and environmental impact.

[0294] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0295] Step 1:

[0296] The user scans the product's barcode using the device's camera. The input in this step is the product's barcode information, and the device converts this input data into a digital code and sends it to the server.

[0297] Step 2:

[0298] The server queries a product characteristics database based on the received barcode information to obtain environmental impact information for the relevant product. The input is barcode information, and the output is environmental impact information obtained from the product characteristics database. This database matching provides specific environmental impact indicators related to the product.

[0299] Step 3:

[0300] The server analyzes the environmental impact information it has acquired and uses a generated AI model to simulate future environmental impacts. In this step, the AI ​​model uses the input environmental impact information to generate predictive images of future environmental changes, following the prompt message: "When a user selects a specific product, visualize and present the impact that product will have on the environment, based on past data."

[0301] Step 4:

[0302] The server sends the environmental simulation results it generates to the terminal. The input here is the simulation result, and the output is image data formatted in a visually easy-to-understand format. The terminal receives this data and prepares to provide feedback to the user in the next phase.

[0303] Step 5:

[0304] The device displays visual feedback on environmental impact information to the user. The input is visual data sent from the server, and the output is environmental impact visualization information displayed on the screen. This allows the user to visually understand the potential environmental impact of their consumption behavior in the future.

[0305] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0306] This invention is an eco-activity system that records users' environmental improvement actions, provides more effective feedback using an emotion engine, and promotes user behavior. In this system, users input their daily eco-activities using a mobile device or computer terminal, and this data is transmitted to a server.

[0307] The server analyzes the received behavioral data and quantifies the specific impact on the environment. This includes, for example, the amount of CO2 reduction and energy consumption. Next, the generative AI module simulates the future environmental state based on the analysis results. This shows the long-term environmental impact predicted if the user's eco-friendly behavior continues. The generated future environmental state is sent from the server to the user's terminal in a visual form.

[0308] When an emotion engine is added here, more abundant feedback becomes possible. The emotion engine recognizes the user's emotional state and customizes the feedback content. For example, when the user has a positive emotion, specific advice for eliciting further eco-friendly behavior or an invitation to a challenge is given. On the other hand, when a negative emotion is recognized, support is provided to make the user feel positive, such as lowering the hurdle of the proposed action or sending a message to reconfirm the importance of eco-friendly behavior.

[0309] As a specific example, when the user inputs "I used a bicycle when going out today", the server analyzes the environmental impact of this behavior, and the emotion engine gives feedback considering the user's emotion at that time. If the user is judged to have a sense of achievement, a message such as "Keep it up! Further reduction of 100 kg of CO2 can be expected." will be displayed. Conversely, if it is judged that the motivation is low, an encouraging message such as "Even small actions piled up are important. You took a great step today." will be presented.

[0310] With this system, the user can not only receive a simple notification of the eco-achievement level, but also receive customized feedback according to their emotions at that time, making it easier to maintain motivation. This promotes continuous environmental improvement actions and is expected to lead to deeper participation in the community.

[0311] The following explains the processing flow.

[0312] Step 1:

[0313] The user launches an application on their device and enters details of their eco-friendly actions for the day, such as "I went shopping by bicycle."

[0314] Step 2:

[0315] The terminal formats the entered eco-behavior data and verifies its integrity. If there are no problems, it prepares to send the data to the server.

[0316] Step 3:

[0317] The server receives eco-behavior data transmitted from the device. The received data is stored in a database and passed to the analysis module in real time.

[0318] Step 4:

[0319] The server uses a behavioral analysis module to quantify the specific environmental impact of users' eco-friendly behaviors, such as the amount of CO2 reduction.

[0320] Step 5:

[0321] After the analysis results are obtained, the server uses a generation AI module to simulate future environmental conditions. It generates predictions of environmental changes if the user continues to take action.

[0322] Step 6:

[0323] The generated environmental simulation images are sent from the server to the terminal. This includes visual feedback that visualizes the long-term impact of the user's eco-friendly behavior.

[0324] Step 7:

[0325] The terminal displays the received simulation image to the user. Simultaneously, it activates the emotion engine to provide feedback to the user.

[0326] Step 8:

[0327] The emotion engine analyzes the user's emotional state through the device. Based on the user's input and actions, it determines the emotional tone.

[0328] Step 9:

[0329] The server receives information from the emotion engine and generates feedback messages appropriate to the user's emotional state.

[0330] Step 10:

[0331] The feedback messages sent from the server to the terminal will be tailored to the user's emotions and designed to motivate them to engage in eco-friendly behavior.

[0332] Step 11:

[0333] Users can view emotion-based feedback on their devices and plan their future eco-friendly actions. They can also interact with other users through community features.

[0334] (Example 2)

[0335] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0336] Conventional environmental improvement support systems have struggled to provide sufficient motivation for users to voluntarily continue eco-friendly behavior, and their feedback is not tailored to individual circumstances or emotions, limiting their ability to encourage sustained user action. Furthermore, they have problems with insufficient quantification and visualization of environmental impact, making it difficult for users to understand the concrete effects of their actions.

[0337] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0338] In this invention, the server includes means for receiving environmental improvement behavior information from the user, means for analyzing the received behavior information and quantifying its impact on the environment, and means for using a generation method to simulate future environmental conditions based on the analysis results. This allows the user to accurately understand the specific impact their actions have on the environment, and by providing feedback on this information according to their individual emotional state, they can continue to improve their behavior more effectively.

[0339] A "user" is an individual who uses this system to record their own environmental improvement actions and receive feedback.

[0340] "Environmental improvement activity information" refers to data on eco-friendly activities that users engage in in their daily lives.

[0341] "Means" refers to the apparatus or method used to achieve a specific objective.

[0342] "Methods for analyzing behavioral information and quantifying its impact on the environment" refers to the process of converting user-inputted behavioral information into numerical values ​​and expressing the impact of those actions on the environment as concrete numerical data.

[0343] "Methods using generational techniques" refers to the process of estimating future environmental conditions using predictive algorithms and simulation techniques.

[0344] "Means of visual conversion and output" refers to technologies that convert numerical data and simulation results into graphs and charts that are easy for users to understand and display.

[0345] "Means of recognizing emotional states and adjusting feedback accordingly" refers to a process that analyzes the user's current emotions and provides optimal messages and advice based on those results.

[0346] "Means of providing information analysis and feedback to users through a secure channel" refers to the process of transmitting analysis results and feedback to users using a secure communication protocol.

[0347] This invention is a system designed to promote environmentally friendly behavior, supporting sustainable eco-activities by analyzing user behavior data and providing personalized feedback.

[0348] Users input their daily eco-friendly activities using mobile or computer devices. This input includes text data such as, for example, "I used my bicycle when I went out today." The device sends the input data to a server. This communication typically uses an internet connection.

[0349] The server uses data analysis libraries (e.g., Pandas, NumPy) to analyze the received behavioral data. This analysis quantifies the impact of the user's eco-friendly behavior on the environment. For example, it may be expressed as a reduction in CO2 emissions or energy consumption.

[0350] Next, the server uses a generative AI model to simulate future environmental states based on user behavior. This process employs machine learning frameworks (e.g., TensorFlow, PyTorch), and the simulated data is transformed into a visual format. This allows users to visually understand how their actions will affect the environment in the future.

[0351] Furthermore, the server is equipped with an emotion engine that uses natural language processing technology to infer the user's emotional state. Based on the user's text input and past behavioral data, it recognizes the user's current emotions and generates corresponding feedback messages. If the user is experiencing positive emotions, a message encouraging more challenging eco-friendly behaviors is generated. On the other hand, if negative emotions are detected, a message emphasizing encouragement and the importance of action is generated.

[0352] Examples of prompts include: "Suggest a feedback message to send when the user is feeling positive emotions," and "Generate specific advice to help the user continue eco-friendly behavior."

[0353] In this way, users can understand the specific impact of their actions and receive personalized feedback, which is expected to promote sustainable eco-friendly activities.

[0354] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0355] Step 1:

[0356] Users input their environmental improvement actions using mobile or computer terminals. Specifically, they input their eco-friendly activities (e.g., "I commuted by bicycle") in text format into the terminal. The terminal checks the integrity of this input data, converts it to the correct format, and then sends it to the server. The input data contains information about observed eco-friendly activities, and the output is a notification that the transmission to the server is complete.

[0357] Step 2:

[0358] The server receives behavioral data sent from the terminal. The received data is stored in a database and then processed using a data analysis library (e.g., Pandas). The server then analyzes the content of the behavior and begins calculations to quantify its impact on the environment. The input is the user's eco-behavior information, and the output is a numerical representation of the impact (e.g., CO2 reduction).

[0359] Step 3:

[0360] The server uses a generative AI model based on the quantified impact results to simulate the predicted future environmental state if the user's eco-friendly behavior continues. This generative process utilizes machine learning algorithms (e.g., TensorFlow) to make long-term predictions. The input is the quantified impact results, and the output is numerical data of the predicted environmental state.

[0361] Step 4:

[0362] The server visualizes the simulation results. It converts the generated numerical data into graphs and charts using a graphics library, making it easy for the user to understand. The input is numerical data of the predicted environmental state, and the output is visualized environmental prediction information.

[0363] Step 5:

[0364] The server analyzes the user's emotional state using an emotion engine and generates appropriate feedback. It analyzes text data using natural language processing techniques to recognize the user's current emotions. Inputs include past behavioral data and user text comments, while output is an emotion-based feedback message.

[0365] Step 6:

[0366] The server sends generated feedback messages and visualized environmental forecast information to the terminal via a secure protocol. Users receive this feedback and information on their terminal and use it to understand the results of their eco-friendly actions and to motivate themselves for the next steps. The input is the feedback message and visualized information, and the output is the display on the user's terminal.

[0367] (Application Example 2)

[0368] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0369] There is a need for a feedback system that streamlines users' environmental improvement actions and promotes sustainable eco-activities. This invention aims to maintain user motivation and promote sustainable behavioral change by recognizing the user's emotional state and providing customized feedback accordingly.

[0370] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0371] In this invention, the server includes means for receiving environmental improvement behavior data from the user, means for analyzing the received behavior data and quantifying its impact on the environment, and means for using a recognition engine to identify the emotional state and adjust the feedback according to that emotion. This makes it possible to customize the feedback based on the user's emotional state.

[0372] "Environmental improvement behavior data" refers to information about environmentally friendly actions taken by users, such as records of actions aimed at saving energy or reducing carbon dioxide emissions.

[0373] "Quantification" refers to the process of converting analyzed data into specific numerical values, making it possible to evaluate it quantitatively.

[0374] A "generative algorithm" is a computational procedure or method used to predict future environmental conditions based on the results of data analysis.

[0375] "Visually displaying" means providing generated information in a way that is easy for the user to understand, using a visual medium such as a display.

[0376] "Feedback" refers to evaluations and advice given to users regarding their actions, serving as motivation to promote or improve their future behavior.

[0377] "Emotional state" refers to the mental state or mood a user is experiencing at a particular moment, and it is a factor that influences their behavior.

[0378] A "recognition engine" refers to technology used to analyze a user's emotions and behavior, and is a mechanism for determining the emotions a user is experiencing.

[0379] This system works by collecting environmental improvement behavior data using users' smart devices and computers and sending it to the cloud. When users input their daily eco-friendly actions, this data is sent to the server. The server analyzes this data and quantifies its environmental impact. This quantification includes CO2 reduction and energy consumption.

[0380] Based on the analysis results, the server simulates future environmental conditions using a generated AI model. The generated future environmental conditions are sent to the user's device as visual content. This content is displayed on the user's smart device or computer display in an intuitively easy-to-understand format.

[0381] Furthermore, this system uses an emotion recognition engine to identify the user's emotional state. This is done through facial expression and voice analysis. Based on this emotional state, the system fine-tunes the feedback. If positive emotions are identified, it encourages more challenging eco-activities; if negative emotions are identified, it suggests more realistic and achievable activities. This strengthens the user's motivation to consistently engage in environmental improvement behaviors.

[0382] For example, if a user enters "I commuted by train today," the server analyzes the environmental impact of this action, and if the emotion recognition engine determines that the user has achieved something, it provides a message such as, "Your train commute reduced CO2 emissions by 20 kg. That's great! If you keep it up, you can reach 100 kg per month!" In this example, the feedback is appropriately adjusted to maintain the user's motivation.

[0383] An example of a prompt message would be: "Visually represent the CO2 reduction effect of a user choosing the train as an eco-friendly activity in 100 characters or less. Also, add an encouraging message if you determine that the user has achieved a sense of accomplishment." This is how the input is given to the generating AI model.

[0384] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0385] Step 1:

[0386] Users input data on their daily environmental improvement actions using a smart device or computer. The input data includes information about specific actions (e.g., cycling to work, using reusable items) and is sent to a server.

[0387] Step 2:

[0388] The server analyzes the received behavioral data and quantifies its environmental impact. This quantification includes CO2 reduction and energy consumption reduction effects. Using the received behavioral data as input, a specific transformation algorithm is applied to output concrete environmental indicators. This process measures the extent to which user behavior contributes to the environment.

[0389] Step 3:

[0390] The server uses a generative AI model to simulate future environmental conditions based on the analysis results. It takes quantified environmental impact data as input, the AI ​​performs the simulation, and outputs environmental visual data, including future predictions. This visualizes the long-term contribution to the environment.

[0391] Step 4:

[0392] The server uses an emotion recognition engine to identify the user's emotional state. It takes facial expression data and voice data acquired from the device's camera and microphone as input, analyzes it, and outputs the user's emotional state. This reveals what emotions the user is currently experiencing.

[0393] Step 5:

[0394] The server generates feedback based on simulation results and the user's emotional state. Using simulation results and emotional data as input, it generates prompts and outputs customized feedback messages using a generation AI model. The system operates by adjusting the feedback accurately to match the user's emotions.

[0395] Step 6:

[0396] The terminal displays feedback messages and visual content generated by the server to the user. It takes data received from the server as input and outputs it to the screen in a visually easy-to-understand format. This allows the user to visually confirm their environmental improvement actions and motivates them to take further action.

[0397] 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0398] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0399] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart glasses 214.

[0400] [Third Embodiment]

[0401] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0402] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0403] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0404] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0405] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0406] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0407] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0408] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0409] The specific processing program 56 is an example of a "program" relating to the technology of this 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 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.

[0410] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0411] In the headset terminal 314, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0412] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the headset terminal 314 will be referred to as the "terminal".

[0413] This invention is a system that provides visual feedback on the impact of environmentally conscious actions taken by users, based on their input. This system primarily uses a terminal, server, and generating AI to record and analyze the user's eco-friendly behavior and predict future environmental conditions.

[0414] Specifically, users first install a dedicated application and input their daily eco-activities, such as commuting by bicycle or recycling, into their device. This behavioral data is then sent from the device to a server. The server receives this data and uses an action analysis module to calculate the specific environmental effects of these eco-activities, such as the amount of carbon dioxide reduced.

[0415] Subsequently, the AI ​​generates simulations of future environmental conditions based on the analysis results. Specifically, it predicts changes in carbon dioxide concentration and average temperature if the user's actions continue for a certain period, and generates these changes in a visual format. The resulting simulation images are then sent to the device in a way that makes the effects easy to understand visually.

[0416] The device displays eco-points to the user along with a generated image of the future environmental state, thereby increasing motivation for action. It also provides an interface that allows users to participate in eco-action challenges and compare their results with those of other users. In this way, users can concretely understand how their actions contribute to the future and gain motivation to promote continuous eco-activities.

[0417] For example, if a user uses public transportation for their commute for a month, the system calculates the amount of carbon dioxide that can be reduced during that period and visualizes the global environmental changes that would occur if this continued for a year. This feedback helps users realize the impact their daily actions have on the planet and provides them with an opportunity to continue being more proactive in eco-friendly behavior.

[0418] The following describes the processing flow.

[0419] Step 1:

[0420] Users launch an application on their device and input their daily eco-friendly activities. For example, they record specific actions such as "I commuted by bicycle today" in an input form.

[0421] Step 2:

[0422] The terminal converts the entered behavioral data into the appropriate format. It verifies the data's integrity and prompts the user to correct the input if necessary. It then prepares the formatted data for transmission to the server.

[0423] Step 3:

[0424] The server receives data sent from the terminal. The data is then passed to the behavioral analysis module in real time. The received data is also stored in a database, making it available for future analysis and trend analysis.

[0425] Step 4:

[0426] The server uses a behavioral analysis module to quantify the specific environmental impact of the user's eco-friendly behavior based on the received data. For example, it calculates CO2 reduction amounts and the rate of reduction in electricity consumption.

[0427] Step 5:

[0428] Based on the analysis results, the server uses generated AI to perform future environmental simulations. The simulations predict long-term environmental changes if users continue their eco-friendly behaviors.

[0429] Step 6:

[0430] Images and graphics representing the generated future environmental state are sent from the server to the terminal. A visually easy-to-understand format is used, and measures are taken to provide clear feedback to the user.

[0431] Step 7:

[0432] The terminal displays simulation images received from the server to the user. It also displays the eco-points the user has earned, providing an incentive for their actions.

[0433] Step 8:

[0434] Users will use the displayed feedback to continue or improve their eco-friendly behaviors. In some cases, they will participate in challenges with other users and share their results to further promote eco-friendly activities.

[0435] (Example 1)

[0436] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0437] Currently, many individuals are taking action to contribute to environmental protection, but there is a problem in that it is difficult to recognize the specific extent of improvement that these actions are leading to. Furthermore, there is a lack of means to visualize the process by which individual activities accumulate and lead to global environmental improvement. For this reason, there is a need to build a system that provides quantitative and visual feedback on the impact of individual activities on the future environment.

[0438] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0439] In this invention, the server includes means for receiving activity information from users, means for analyzing the received activity information and quantifying its impact on the environment, and means for using a generative model to predict future environmental conditions based on the analysis results. This allows users to concretely understand the impact their activities will have on the future environment and receive feedback that enhances their motivation for eco-friendly activities.

[0440] A "user" is an individual or organization that uses the system to input activity information and receives environmental impact information.

[0441] "Activity information" refers to data about environmental protection activities carried out by users, and is information received by the system.

[0442] "Means of receiving" refers to the function that allows the server to retrieve activity information entered by the user.

[0443] "Means of analysis and quantification" refers to the process of expressing the impact on the environment as specific numerical values ​​based on the received activity information.

[0444] "Methods using generative models" refer to methods for simulating and predicting future environmental conditions using analyzed data.

[0445] "Means of visual output" refers to functions that represent predictive information obtained by generative models in a format that is easy for users to understand, such as graphs and images.

[0446] "Means of reporting" refers to the process of notifying and explaining to users the environmental impact of their actions based on visually generated predictive information.

[0447] To implement this invention, a terminal, a server, and a generative AI model are required.

[0448] Users first install a specific application software on their device and input information about their daily environmental protection activities. This app is designed to run on smartphones and tablets. By inputting data on specific environmental protection activities, such as the number of days they commuted by bicycle or the details of their recycling, eco-activity data is accumulated on their device. This input can be easily done by users using touch panels or voice recognition.

[0449] The terminal sends the input data to the server. The server records the received data using a database system and analyzes it using an activity analysis module. The activity analysis module includes algorithms built in specific programming languages ​​(such as Python or Java) that quantitatively evaluate the environmental impact (e.g., carbon dioxide reduction) based on the input activity information.

[0450] Next, the server uses a generative AI model to simulate future environmental conditions. This model is developed using machine learning frameworks (such as TensorFlow and PyTorch). It receives the analyzed data as input and predicts how the user's continued eco-friendly activities will impact the global economy. The server provides the generative AI with prompts such as, "Visualize the carbon dioxide reduction effect if the user commutes by bicycle for one year."

[0451] The simulation results are generated as visual content. For example, graphs showing the predicted decrease in carbon dioxide concentration and images showing the trend of future average temperature fluctuations are generated. This allows users to intuitively understand the impact their activities will have on the future environment.

[0452] The device displays eco-points to the user for their activities, along with visual feedback. This provides a mechanism to maintain and improve user motivation, and also includes an interface for comparing results with other users. This is achieved by utilizing integration with social platforms and other similar systems.

[0453] This system configuration makes it possible to accurately understand how individual eco-activities contribute to the future environment, and to provide ecological action support that is easy for users to understand and that increases their motivation to participate.

[0454] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0455] Step 1:

[0456] Users install a dedicated application on their device and input information about their daily environmental protection activities. For example, users enter data such as "I commuted by bicycle today" into an input form. The entered information is temporarily stored in a database on the device. At this stage, the input data is retained in its original format.

[0457] Step 2:

[0458] The device transmits the entered activity information to the server via the internet. Encryption is applied to protect the information during this transmission. The server receives this data and records it in a database. The received data is stored as structured data, including elements such as activity type, date, and frequency.

[0459] Step 3:

[0460] The server passes the received data to the analysis module. The analysis module uses an algorithm to quantify the environmental impact (e.g., carbon dioxide reduction) based on the input activity information. In this process, specific reduction amounts are calculated using historical trend data and environmental coefficients. The output provides the reduction amount and the quantified effect of eco-activities.

[0461] Step 4:

[0462] The server uses a generative AI model to simulate future environmental conditions based on the analysis results. The server sends prompts to the generative AI, such as "Visualize the carbon dioxide reduction effect if a user commutes by bicycle for one year," and initiates the simulation. The generative AI model receives the analysis results as input, makes predictions about future trends, and expresses them as visual content. The output here includes graphs and images showing the predicted environmental conditions.

[0463] Step 5:

[0464] The device displays visual feedback and eco-point information sent back from the server to the user. The screen shows a graph of predicted future environmental changes along with a message such as, "You have earned XX points for your activities." This allows users to intuitively understand how their actions have a concrete environmental impact. This feedback serves to motivate users to continue their environmental activities.

[0465] (Application Example 1)

[0466] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0467] Building a sustainable society requires individuals to recognize the impact their daily actions have on the environment and to encourage more environmentally conscious choices. However, consumers lack visible means of understanding the environmental impact of products, which makes it difficult to translate this into concrete behavioral changes.

[0468] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0469] In this invention, the server includes means for receiving environmental improvement action data from the user, means for acquiring product identification information and acquiring environmental impact information from a product characteristics database, and means for providing the user with environmental impact information and providing feedback on the eco-footprint. This makes it possible for consumers to immediately understand the environmental impact of the products they purchase and to visually grasp the impact their choices will have on the future environment.

[0470] A "user" is a person who uses this system to record and analyze environmental improvement actions.

[0471] "Environmental improvement behavior data" refers to information about environmentally conscious behaviors provided by users.

[0472] A "generative algorithm" is a computational procedure used to simulate future environmental conditions based on received data.

[0473] "Product identification information" refers to data codes or barcode information used to identify a specific product.

[0474] A "product characteristics database" is a collection of data that records environmental impact information and other data related to each product.

[0475] "Environmental impact information" refers to numerical data about the impact that a product has on the environment during its manufacturing, distribution, and consumption processes.

[0476] "Eco-footprint" is a numerical or visual indicator that represents the impact of a user's consumption behavior on the environment.

[0477] To realize this invention, a server system operating on the cloud and a user-operable terminal device are required. The server receives environmental improvement action data from the user, analyzes the data, and quantifies its impact on the environment. The quantified data is used to simulate future environmental conditions using a generation algorithm. In this process, a generation AI model is utilized to perform highly accurate predictions.

[0478] When a user provides product identification information through their device, the server retrieves relevant environmental impact information from a product characteristics database. This allows for the calculation of a specific eco-footprint, which is then fed back to the user. The device is equipped with a camera for identification (e.g., a smartphone's camera API) and a display for displaying visual information (smart glasses or a display unit).

[0479] As a concrete example, when a user purchases organic food at a supermarket, they scan the product's barcode using their smartphone camera. Based on this information, the server retrieves the product's eco-footprint information from a product characteristics database and visualizes the calculated environmental impact, displaying it on the user's device. This feedback allows the user to instantly understand the environmental impact of their purchase.

[0480] The generative AI model operates based on the prompt statement, "When a user selects a specific product, the AI ​​will visualize and present the environmental impact of that product based on past data."

[0481] The hardware used includes smartphones and smart glasses, while the software includes data processing modules using Python and generative AI models using TensorFlow and PyTorch. By combining these technologies, a system will be built that enables visual feedback on consumer behavior and environmental impact.

[0482] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0483] Step 1:

[0484] The user scans the product's barcode using the device's camera. The input in this step is the product's barcode information, and the device converts this input data into a digital code and sends it to the server.

[0485] Step 2:

[0486] The server queries a product characteristics database based on the received barcode information to obtain environmental impact information for the relevant product. The input is barcode information, and the output is environmental impact information obtained from the product characteristics database. This database matching provides specific environmental impact indicators related to the product.

[0487] Step 3:

[0488] The server analyzes the environmental impact information it has acquired and uses a generated AI model to simulate future environmental impacts. In this step, the AI ​​model uses the input environmental impact information to generate predictive images of future environmental changes, following the prompt message: "When a user selects a specific product, visualize and present the impact that product will have on the environment, based on past data."

[0489] Step 4:

[0490] The server sends the environmental simulation results it generates to the terminal. The input here is the simulation result, and the output is image data formatted in a visually easy-to-understand format. The terminal receives this data and prepares to provide feedback to the user in the next phase.

[0491] Step 5:

[0492] The device displays visual feedback on environmental impact information to the user. The input is visual data sent from the server, and the output is environmental impact visualization information displayed on the screen. This allows the user to visually understand the potential environmental impact of their consumption behavior in the future.

[0493] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0494] This invention is an eco-activity system that records users' environmental improvement actions, provides more effective feedback using an emotion engine, and promotes user behavior. In this system, users input their daily eco-activities using a mobile device or computer terminal, and this data is transmitted to a server.

[0495] The server analyzes the received behavioral data and quantifies the specific environmental impact. This includes, for example, CO2 reduction and energy consumption. Next, the generating AI module simulates future environmental conditions based on the analysis results. This shows the long-term environmental impact predicted if the user's eco-friendly behavior continues. The generated future environmental conditions are sent from the server to the user's terminal in a visual format.

[0496] The addition of an emotion engine enables even richer feedback. The emotion engine recognizes the user's emotional state and customizes the feedback accordingly. For example, if the user is experiencing positive emotions, it provides specific advice or invitations to challenges to encourage further eco-friendly behavior. On the other hand, if negative emotions are detected, it lowers the barriers to suggested actions or sends messages reaffirming the importance of eco-friendly behavior, supporting the user in developing a more positive mindset.

[0497] For example, if a user enters "I used my bicycle when I went out today," the server analyzes the environmental impact of this action, and the emotion engine provides feedback considering the user's emotions at that time. If the system determines that the user has achieved something, a message such as "Keep it up! You can expect to reduce CO2 emissions by another 100 kg" will be displayed. Conversely, if the system determines that the user's motivation is low, an encouraging message such as "Even small actions add up. You took a great first step today" will be presented.

[0498] This system allows users to not only receive notifications about their eco-achievement levels, but also personalized feedback tailored to their emotions at the time, making it easier to maintain motivation. This is expected to promote sustainable environmental improvement actions and foster deeper community engagement.

[0499] The following describes the processing flow.

[0500] Step 1:

[0501] The user launches an application on their device and enters details of their eco-friendly actions for the day, such as "I went shopping by bicycle."

[0502] Step 2:

[0503] The terminal formats the entered eco-behavior data and verifies its integrity. If there are no problems, it prepares to send the data to the server.

[0504] Step 3:

[0505] The server receives eco-behavior data transmitted from the device. The received data is stored in a database and passed to the analysis module in real time.

[0506] Step 4:

[0507] The server uses a behavioral analysis module to quantify the specific environmental impact of users' eco-friendly behaviors, such as the amount of CO2 reduction.

[0508] Step 5:

[0509] After the analysis results are obtained, the server uses a generation AI module to simulate future environmental conditions. It generates predictions of environmental changes if the user continues to take action.

[0510] Step 6:

[0511] The generated environmental simulation images are sent from the server to the terminal. This includes visual feedback that visualizes the long-term impact of the user's eco-friendly behavior.

[0512] Step 7:

[0513] The terminal displays the received simulation image to the user. Simultaneously, it activates the emotion engine to provide feedback to the user.

[0514] Step 8:

[0515] The emotion engine analyzes the user's emotional state through the device. Based on the user's input and actions, it determines the emotional tone.

[0516] Step 9:

[0517] The server receives information from the emotion engine and generates feedback messages appropriate to the user's emotional state.

[0518] Step 10:

[0519] The feedback messages sent from the server to the terminal will be tailored to the user's emotions and designed to motivate them to engage in eco-friendly behavior.

[0520] Step 11:

[0521] Users can view emotion-based feedback on their devices and plan their future eco-friendly actions. They can also interact with other users through community features.

[0522] (Example 2)

[0523] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0524] Conventional environmental improvement support systems have struggled to provide sufficient motivation for users to voluntarily continue eco-friendly behavior, and their feedback is not tailored to individual circumstances or emotions, limiting their ability to encourage sustained user action. Furthermore, they have problems with insufficient quantification and visualization of environmental impact, making it difficult for users to understand the concrete effects of their actions.

[0525] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0526] In this invention, the server includes means for receiving environmental improvement behavior information from the user, means for analyzing the received behavior information and quantifying its impact on the environment, and means for using a generation method to simulate future environmental conditions based on the analysis results. This allows the user to accurately understand the specific impact their actions have on the environment, and by providing feedback on this information according to their individual emotional state, they can continue to improve their behavior more effectively.

[0527] A "user" is an individual who uses this system to record their own environmental improvement actions and receive feedback.

[0528] "Environmental improvement activity information" refers to data on eco-friendly activities that users engage in in their daily lives.

[0529] "Means" refers to the apparatus or method used to achieve a specific objective.

[0530] "Methods for analyzing behavioral information and quantifying its impact on the environment" refers to the process of converting user-inputted behavioral information into numerical values ​​and expressing the impact of those actions on the environment as concrete numerical data.

[0531] "Methods using generational techniques" refers to the process of estimating future environmental conditions using predictive algorithms and simulation techniques.

[0532] "Means of visual conversion and output" refers to technologies that convert numerical data and simulation results into graphs and charts that are easy for users to understand and display.

[0533] "Means of recognizing emotional states and adjusting feedback accordingly" refers to a process that analyzes the user's current emotions and provides optimal messages and advice based on those results.

[0534] "Means of providing information analysis and feedback to users through a secure channel" refers to the process of transmitting analysis results and feedback to users using a secure communication protocol.

[0535] This invention is a system designed to promote environmentally friendly behavior, supporting sustainable eco-activities by analyzing user behavior data and providing personalized feedback.

[0536] Users input their daily eco-friendly activities using mobile or computer devices. This input includes text data such as, for example, "I used my bicycle when I went out today." The device sends the input data to a server. This communication typically uses an internet connection.

[0537] The server uses data analysis libraries (e.g., Pandas, NumPy) to analyze the received behavioral data. This analysis quantifies the impact of the user's eco-friendly behavior on the environment. For example, it may be expressed as a reduction in CO2 emissions or energy consumption.

[0538] Next, the server uses a generative AI model to simulate future environmental states based on user behavior. This process employs machine learning frameworks (e.g., TensorFlow, PyTorch), and the simulated data is transformed into a visual format. This allows users to visually understand how their actions will affect the environment in the future.

[0539] Furthermore, the server is equipped with an emotion engine that uses natural language processing technology to infer the user's emotional state. Based on the user's text input and past behavioral data, it recognizes the user's current emotions and generates corresponding feedback messages. If the user is experiencing positive emotions, a message encouraging more challenging eco-friendly behaviors is generated. On the other hand, if negative emotions are detected, a message emphasizing encouragement and the importance of action is generated.

[0540] Examples of prompts include: "Suggest a feedback message to send when the user is feeling positive emotions," and "Generate specific advice to help the user continue eco-friendly behavior."

[0541] In this way, users can understand the specific impact of their actions and receive personalized feedback, which is expected to promote sustainable eco-friendly activities.

[0542] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0543] Step 1:

[0544] Users input their environmental improvement actions using mobile or computer terminals. Specifically, they input their eco-friendly activities (e.g., "I commuted by bicycle") in text format into the terminal. The terminal checks the integrity of this input data, converts it to the correct format, and then sends it to the server. The input data contains information about observed eco-friendly activities, and the output is a notification that the transmission to the server is complete.

[0545] Step 2:

[0546] The server receives behavioral data sent from the terminal. The received data is stored in a database and then processed using a data analysis library (e.g., Pandas). The server then analyzes the content of the behavior and begins calculations to quantify its impact on the environment. The input is the user's eco-behavior information, and the output is a numerical representation of the impact (e.g., CO2 reduction).

[0547] Step 3:

[0548] The server uses a generative AI model based on the quantified impact results to simulate the predicted future environmental state if the user's eco-friendly behavior continues. This generative process utilizes machine learning algorithms (e.g., TensorFlow) to make long-term predictions. The input is the quantified impact results, and the output is numerical data of the predicted environmental state.

[0549] Step 4:

[0550] The server visualizes the simulation results. It converts the generated numerical data into graphs and charts using a graphics library, making it easy for the user to understand. The input is numerical data of the predicted environmental state, and the output is visualized environmental prediction information.

[0551] Step 5:

[0552] The server analyzes the user's emotional state using an emotion engine and generates appropriate feedback. It analyzes text data using natural language processing techniques to recognize the user's current emotions. Inputs include past behavioral data and user text comments, while output is an emotion-based feedback message.

[0553] Step 6:

[0554] The server sends generated feedback messages and visualized environmental forecast information to the terminal via a secure protocol. Users receive this feedback and information on their terminal and use it to understand the results of their eco-friendly actions and to motivate themselves for the next steps. The input is the feedback message and visualized information, and the output is the display on the user's terminal.

[0555] (Application Example 2)

[0556] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0557] There is a need for a feedback system that streamlines users' environmental improvement actions and promotes sustainable eco-activities. This invention aims to maintain user motivation and promote sustainable behavioral change by recognizing the user's emotional state and providing customized feedback accordingly.

[0558] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0559] In this invention, the server includes means for receiving environmental improvement behavior data from the user, means for analyzing the received behavior data and quantifying its impact on the environment, and means for using a recognition engine to identify the emotional state and adjust the feedback according to that emotion. This makes it possible to customize the feedback based on the user's emotional state.

[0560] "Environmental improvement behavior data" refers to information about environmentally friendly actions taken by users, such as records of actions aimed at saving energy or reducing carbon dioxide emissions.

[0561] "Quantification" refers to the process of converting analyzed data into specific numerical values, making it possible to evaluate it quantitatively.

[0562] A "generative algorithm" is a computational procedure or method used to predict future environmental conditions based on the results of data analysis.

[0563] "Visually displaying" means providing generated information in a way that is easy for the user to understand, using a visual medium such as a display.

[0564] "Feedback" refers to evaluations and advice given to users regarding their actions, serving as motivation to promote or improve their future behavior.

[0565] "Emotional state" refers to the mental state or mood a user is experiencing at a particular moment, and it is a factor that influences their behavior.

[0566] A "recognition engine" refers to technology used to analyze a user's emotions and behavior, and is a mechanism for determining the emotions a user is experiencing.

[0567] This system works by collecting environmental improvement behavior data using users' smart devices and computers and sending it to the cloud. When users input their daily eco-friendly actions, this data is sent to the server. The server analyzes this data and quantifies its environmental impact. This quantification includes CO2 reduction and energy consumption.

[0568] Based on the analysis results, the server simulates future environmental conditions using a generated AI model. The generated future environmental conditions are sent to the user's device as visual content. This content is displayed on the user's smart device or computer display in an intuitively easy-to-understand format.

[0569] Furthermore, this system uses an emotion recognition engine to identify the user's emotional state. This is done through facial expression and voice analysis. Based on this emotional state, the system fine-tunes the feedback. If positive emotions are identified, it encourages more challenging eco-activities; if negative emotions are identified, it suggests more realistic and achievable activities. This strengthens the user's motivation to consistently engage in environmental improvement behaviors.

[0570] For example, if a user enters "I commuted by train today," the server analyzes the environmental impact of this action, and if the emotion recognition engine determines that the user has achieved something, it provides a message such as, "Your train commute reduced CO2 emissions by 20 kg. That's great! If you keep it up, you can reach 100 kg per month!" In this example, the feedback is appropriately adjusted to maintain the user's motivation.

[0571] An example of a prompt message would be: "Visually represent the CO2 reduction effect of a user choosing the train as an eco-friendly activity in 100 characters or less. Also, add an encouraging message if you determine that the user has achieved a sense of accomplishment." This is how the input is given to the generating AI model.

[0572] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0573] Step 1:

[0574] Users input data on their daily environmental improvement actions using a smart device or computer. The input data includes information about specific actions (e.g., cycling to work, using reusable items) and is sent to a server.

[0575] Step 2:

[0576] The server analyzes the received behavioral data and quantifies its environmental impact. This quantification includes CO2 reduction and energy consumption reduction effects. Using the received behavioral data as input, a specific transformation algorithm is applied to output concrete environmental indicators. This process measures the extent to which user behavior contributes to the environment.

[0577] Step 3:

[0578] The server uses a generative AI model to simulate future environmental conditions based on the analysis results. It takes quantified environmental impact data as input, the AI ​​performs the simulation, and outputs environmental visual data, including future predictions. This visualizes the long-term contribution to the environment.

[0579] Step 4:

[0580] The server uses an emotion recognition engine to identify the user's emotional state. It takes facial expression data and voice data acquired from the device's camera and microphone as input, analyzes it, and outputs the user's emotional state. This reveals what emotions the user is currently experiencing.

[0581] Step 5:

[0582] The server generates feedback based on simulation results and the user's emotional state. Using simulation results and emotional data as input, it generates prompts and outputs customized feedback messages using a generation AI model. The system operates by adjusting the feedback accurately to match the user's emotions.

[0583] Step 6:

[0584] The terminal displays feedback messages and visual content generated by the server to the user. It takes data received from the server as input and outputs it to the screen in a visually easy-to-understand format. This allows the user to visually confirm their environmental improvement actions and motivates them to take further action.

[0585] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0586] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0587] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and specific processing may also be performed by the headset terminal 314.

[0588] [Fourth Embodiment]

[0589] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0590] As shown in Figure 7, the 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.

[0591] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0592] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0593] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0594] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0595] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0596] The controlled 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 robot 414's emotions can be expressed by controlling these motors. Furthermore, the robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0597] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0598] The specific processing program 56 is an example of a "program" relating to the technology of this 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 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.

[0599] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0600] In robot 414, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0601] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0602] This invention is a system that provides visual feedback on the impact of environmentally conscious actions taken by users, based on their input. This system primarily uses a terminal, server, and generating AI to record and analyze the user's eco-friendly behavior and predict future environmental conditions.

[0603] Specifically, users first install a dedicated application and input their daily eco-activities, such as commuting by bicycle or recycling, into their device. This behavioral data is then sent from the device to a server. The server receives this data and uses an action analysis module to calculate the specific environmental effects of these eco-activities, such as the amount of carbon dioxide reduced.

[0604] Subsequently, the AI ​​generates simulations of future environmental conditions based on the analysis results. Specifically, it predicts changes in carbon dioxide concentration and average temperature if the user's actions continue for a certain period, and generates these changes in a visual format. The resulting simulation images are then sent to the device in a way that makes the effects easy to understand visually.

[0605] The device displays eco-points to the user along with a generated image of the future environmental state, thereby increasing motivation for action. It also provides an interface that allows users to participate in eco-action challenges and compare their results with those of other users. In this way, users can concretely understand how their actions contribute to the future and gain motivation to promote continuous eco-activities.

[0606] For example, if a user uses public transportation for their commute for a month, the system calculates the amount of carbon dioxide that can be reduced during that period and visualizes the global environmental changes that would occur if this continued for a year. This feedback helps users realize the impact their daily actions have on the planet and provides them with an opportunity to continue being more proactive in eco-friendly behavior.

[0607] The following describes the processing flow.

[0608] Step 1:

[0609] Users launch an application on their device and input their daily eco-friendly activities. For example, they record specific actions such as "I commuted by bicycle today" in an input form.

[0610] Step 2:

[0611] The terminal converts the entered behavioral data into the appropriate format. It verifies the data's integrity and prompts the user to correct the input if necessary. It then prepares the formatted data for transmission to the server.

[0612] Step 3:

[0613] The server receives data sent from the terminal. The data is then passed to the behavioral analysis module in real time. The received data is also stored in a database, making it available for future analysis and trend analysis.

[0614] Step 4:

[0615] The server uses a behavioral analysis module to quantify the specific environmental impact of the user's eco-friendly behavior based on the received data. For example, it calculates CO2 reduction amounts and the rate of reduction in electricity consumption.

[0616] Step 5:

[0617] Based on the analysis results, the server uses generated AI to perform future environmental simulations. The simulations predict long-term environmental changes if users continue their eco-friendly behaviors.

[0618] Step 6:

[0619] Images and graphics representing the generated future environmental state are sent from the server to the terminal. A visually easy-to-understand format is used, and measures are taken to provide clear feedback to the user.

[0620] Step 7:

[0621] The terminal displays simulation images received from the server to the user. It also displays the eco-points the user has earned, providing an incentive for their actions.

[0622] Step 8:

[0623] Users will use the displayed feedback to continue or improve their eco-friendly behaviors. In some cases, they will participate in challenges with other users and share their results to further promote eco-friendly activities.

[0624] (Example 1)

[0625] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0626] Currently, many individuals are taking action to contribute to environmental protection, but there is a problem in that it is difficult to recognize the specific extent of improvement that these actions are leading to. Furthermore, there is a lack of means to visualize the process by which individual activities accumulate and lead to global environmental improvement. For this reason, there is a need to build a system that provides quantitative and visual feedback on the impact of individual activities on the future environment.

[0627] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0628] In this invention, the server includes means for receiving activity information from users, means for analyzing the received activity information and quantifying its impact on the environment, and means for using a generative model to predict future environmental conditions based on the analysis results. This allows users to concretely understand the impact their activities will have on the future environment and receive feedback that enhances their motivation for eco-friendly activities.

[0629] A "user" is an individual or organization that uses the system to input activity information and receives environmental impact information.

[0630] "Activity information" refers to data about environmental protection activities carried out by users, and is information received by the system.

[0631] "Means of receiving" refers to the function that allows the server to retrieve activity information entered by the user.

[0632] "Means of analysis and quantification" refers to the process of expressing the impact on the environment as specific numerical values ​​based on the received activity information.

[0633] "Methods using generative models" refer to methods for simulating and predicting future environmental conditions using analyzed data.

[0634] "Means of visual output" refers to functions that represent predictive information obtained by generative models in a format that is easy for users to understand, such as graphs and images.

[0635] "Means of reporting" refers to the process of notifying and explaining to users the environmental impact of their actions based on visually generated predictive information.

[0636] To implement this invention, a terminal, a server, and a generative AI model are required.

[0637] Users first install a specific application software on their device and input information about their daily environmental protection activities. This app is designed to run on smartphones and tablets. By inputting data on specific environmental protection activities, such as the number of days they commuted by bicycle or the details of their recycling, eco-activity data is accumulated on their device. This input can be easily done by users using touch panels or voice recognition.

[0638] The terminal sends the input data to the server. The server records the received data using a database system and analyzes it using an activity analysis module. The activity analysis module includes algorithms built in specific programming languages ​​(such as Python or Java) that quantitatively evaluate the environmental impact (e.g., carbon dioxide reduction) based on the input activity information.

[0639] Next, the server uses a generative AI model to simulate future environmental conditions. This model is developed using machine learning frameworks (such as TensorFlow and PyTorch). It receives the analyzed data as input and predicts how the user's continued eco-friendly activities will impact the global economy. The server provides the generative AI with prompts such as, "Visualize the carbon dioxide reduction effect if the user commutes by bicycle for one year."

[0640] The simulation results are generated as visual content. For example, graphs showing the predicted decrease in carbon dioxide concentration and images showing the trend of future average temperature fluctuations are generated. This allows users to intuitively understand the impact their activities will have on the future environment.

[0641] The device displays eco-points to the user for their activities, along with visual feedback. This provides a mechanism to maintain and improve user motivation, and also includes an interface for comparing results with other users. This is achieved by utilizing integration with social platforms and other similar systems.

[0642] This system configuration makes it possible to accurately understand how individual eco-activities contribute to the future environment, and to provide ecological action support that is easy for users to understand and that increases their motivation to participate.

[0643] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0644] Step 1:

[0645] Users install a dedicated application on their device and input information about their daily environmental protection activities. For example, users enter data such as "I commuted by bicycle today" into an input form. The entered information is temporarily stored in a database on the device. At this stage, the input data is retained in its original format.

[0646] Step 2:

[0647] The device transmits the entered activity information to the server via the internet. Encryption is applied to protect the information during this transmission. The server receives this data and records it in a database. The received data is stored as structured data, including elements such as activity type, date, and frequency.

[0648] Step 3:

[0649] The server passes the received data to the analysis module. The analysis module uses an algorithm to quantify the environmental impact (e.g., carbon dioxide reduction) based on the input activity information. In this process, specific reduction amounts are calculated using historical trend data and environmental coefficients. The output provides the reduction amount and the quantified effect of eco-activities.

[0650] Step 4:

[0651] The server uses a generative AI model to simulate future environmental conditions based on the analysis results. The server sends prompts to the generative AI, such as "Visualize the carbon dioxide reduction effect if a user commutes by bicycle for one year," and initiates the simulation. The generative AI model receives the analysis results as input, makes predictions about future trends, and expresses them as visual content. The output here includes graphs and images showing the predicted environmental conditions.

[0652] Step 5:

[0653] The device displays visual feedback and eco-point information sent back from the server to the user. The screen shows a graph of predicted future environmental changes along with a message such as, "You have earned XX points for your activities." This allows users to intuitively understand how their actions have a concrete environmental impact. This feedback serves to motivate users to continue their environmental activities.

[0654] (Application Example 1)

[0655] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0656] Building a sustainable society requires individuals to recognize the impact their daily actions have on the environment and to encourage more environmentally conscious choices. However, consumers lack visible means of understanding the environmental impact of products, which makes it difficult to translate this into concrete behavioral changes.

[0657] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0658] In this invention, the server includes means for receiving environmental improvement action data from the user, means for acquiring product identification information and acquiring environmental impact information from a product characteristics database, and means for providing the user with environmental impact information and providing feedback on the eco-footprint. This makes it possible for consumers to immediately understand the environmental impact of the products they purchase and to visually grasp the impact their choices will have on the future environment.

[0659] A "user" is a person who uses this system to record and analyze environmental improvement actions.

[0660] "Environmental improvement behavior data" refers to information about environmentally conscious behaviors provided by users.

[0661] A "generative algorithm" is a computational procedure used to simulate future environmental conditions based on received data.

[0662] "Product identification information" refers to data codes or barcode information used to identify a specific product.

[0663] A "product characteristics database" is a collection of data that records environmental impact information and other data related to each product.

[0664] "Environmental impact information" refers to numerical data about the impact that a product has on the environment during its manufacturing, distribution, and consumption processes.

[0665] "Eco-footprint" is a numerical or visual indicator that represents the impact of a user's consumption behavior on the environment.

[0666] To realize this invention, a server system operating on the cloud and a user-operable terminal device are required. The server receives environmental improvement action data from the user, analyzes the data, and quantifies its impact on the environment. The quantified data is used to simulate future environmental conditions using a generation algorithm. In this process, a generation AI model is utilized to perform highly accurate predictions.

[0667] When a user provides product identification information through their device, the server retrieves relevant environmental impact information from a product characteristics database. This allows for the calculation of a specific eco-footprint, which is then fed back to the user. The device is equipped with a camera for identification (e.g., a smartphone's camera API) and a display for displaying visual information (smart glasses or a display unit).

[0668] As a concrete example, when a user purchases organic food at a supermarket, they scan the product's barcode using their smartphone camera. Based on this information, the server retrieves the product's eco-footprint information from a product characteristics database and visualizes the calculated environmental impact, displaying it on the user's device. This feedback allows the user to instantly understand the environmental impact of their purchase.

[0669] The generative AI model operates based on the prompt statement, "When a user selects a specific product, the AI ​​will visualize and present the environmental impact of that product based on past data."

[0670] The hardware used includes smartphones and smart glasses, while the software includes data processing modules using Python and generative AI models using TensorFlow and PyTorch. By combining these technologies, a system will be built that enables visual feedback on consumer behavior and environmental impact.

[0671] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0672] Step 1:

[0673] The user scans the product's barcode using the device's camera. The input in this step is the product's barcode information, and the device converts this input data into a digital code and sends it to the server.

[0674] Step 2:

[0675] The server queries a product characteristics database based on the received barcode information to obtain environmental impact information for the relevant product. The input is barcode information, and the output is environmental impact information obtained from the product characteristics database. This database matching provides specific environmental impact indicators related to the product.

[0676] Step 3:

[0677] The server analyzes the environmental impact information it has acquired and uses a generated AI model to simulate future environmental impacts. In this step, the AI ​​model uses the input environmental impact information to generate predictive images of future environmental changes, following the prompt message: "When a user selects a specific product, visualize and present the impact that product will have on the environment, based on past data."

[0678] Step 4:

[0679] The server sends the environmental simulation results it generates to the terminal. The input here is the simulation result, and the output is image data formatted in a visually easy-to-understand format. The terminal receives this data and prepares to provide feedback to the user in the next phase.

[0680] Step 5:

[0681] The device displays visual feedback on environmental impact information to the user. The input is visual data sent from the server, and the output is environmental impact visualization information displayed on the screen. This allows the user to visually understand the potential environmental impact of their consumption behavior in the future.

[0682] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0683] This invention is an eco-activity system that records users' environmental improvement actions, provides more effective feedback using an emotion engine, and promotes user behavior. In this system, users input their daily eco-activities using a mobile device or computer terminal, and this data is transmitted to a server.

[0684] The server analyzes the received behavioral data and quantifies the specific environmental impact. This includes, for example, CO2 reduction and energy consumption. Next, the generating AI module simulates future environmental conditions based on the analysis results. This shows the long-term environmental impact predicted if the user's eco-friendly behavior continues. The generated future environmental conditions are sent from the server to the user's terminal in a visual format.

[0685] The addition of an emotion engine enables even richer feedback. The emotion engine recognizes the user's emotional state and customizes the feedback accordingly. For example, if the user is experiencing positive emotions, it provides specific advice or invitations to challenges to encourage further eco-friendly behavior. On the other hand, if negative emotions are detected, it lowers the barriers to suggested actions or sends messages reaffirming the importance of eco-friendly behavior, supporting the user in developing a more positive mindset.

[0686] For example, if a user enters "I used my bicycle when I went out today," the server analyzes the environmental impact of this action, and the emotion engine provides feedback considering the user's emotions at that time. If the system determines that the user has achieved something, a message such as "Keep it up! You can expect to reduce CO2 emissions by another 100 kg" will be displayed. Conversely, if the system determines that the user's motivation is low, an encouraging message such as "Even small actions add up. You took a great first step today" will be presented.

[0687] This system allows users to not only receive notifications about their eco-achievement levels, but also personalized feedback tailored to their emotions at the time, making it easier to maintain motivation. This is expected to promote sustainable environmental improvement actions and foster deeper community engagement.

[0688] The following describes the processing flow.

[0689] Step 1:

[0690] The user launches an application on their device and enters details of their eco-friendly actions for the day, such as "I went shopping by bicycle."

[0691] Step 2:

[0692] The terminal formats the entered eco-behavior data and verifies its integrity. If there are no problems, it prepares to send the data to the server.

[0693] Step 3:

[0694] The server receives eco-behavior data transmitted from the device. The received data is stored in a database and passed to the analysis module in real time.

[0695] Step 4:

[0696] The server uses a behavioral analysis module to quantify the specific environmental impact of users' eco-friendly behaviors, such as the amount of CO2 reduction.

[0697] Step 5:

[0698] After the analysis results are obtained, the server uses a generation AI module to simulate future environmental conditions. It generates predictions of environmental changes if the user continues to take action.

[0699] Step 6:

[0700] The generated environmental simulation images are sent from the server to the terminal. This includes visual feedback that visualizes the long-term impact of the user's eco-friendly behavior.

[0701] Step 7:

[0702] The terminal displays the received simulation image to the user. Simultaneously, it activates the emotion engine to provide feedback to the user.

[0703] Step 8:

[0704] The emotion engine analyzes the user's emotional state through the device. Based on the user's input and actions, it determines the emotional tone.

[0705] Step 9:

[0706] The server receives information from the emotion engine and generates feedback messages appropriate to the user's emotional state.

[0707] Step 10:

[0708] The feedback messages sent from the server to the terminal will be tailored to the user's emotions and designed to motivate them to engage in eco-friendly behavior.

[0709] Step 11:

[0710] Users can view emotion-based feedback on their devices and plan their future eco-friendly actions. They can also interact with other users through community features.

[0711] (Example 2)

[0712] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0713] Conventional environmental improvement support systems have struggled to provide sufficient motivation for users to voluntarily continue eco-friendly behavior, and their feedback is not tailored to individual circumstances or emotions, limiting their ability to encourage sustained user action. Furthermore, they have problems with insufficient quantification and visualization of environmental impact, making it difficult for users to understand the concrete effects of their actions.

[0714] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0715] In this invention, the server includes means for receiving environmental improvement behavior information from the user, means for analyzing the received behavior information and quantifying its impact on the environment, and means for using a generation method to simulate future environmental conditions based on the analysis results. This allows the user to accurately understand the specific impact their actions have on the environment, and by providing feedback on this information according to their individual emotional state, they can continue to improve their behavior more effectively.

[0716] A "user" is an individual who uses this system to record their own environmental improvement actions and receive feedback.

[0717] "Environmental improvement activity information" refers to data on eco-friendly activities that users engage in in their daily lives.

[0718] "Means" refers to the apparatus or method used to achieve a specific objective.

[0719] "Methods for analyzing behavioral information and quantifying its impact on the environment" refers to the process of converting user-inputted behavioral information into numerical values ​​and expressing the impact of those actions on the environment as concrete numerical data.

[0720] "Methods using generational techniques" refers to the process of estimating future environmental conditions using predictive algorithms and simulation techniques.

[0721] "Means of visual conversion and output" refers to technologies that convert numerical data and simulation results into graphs and charts that are easy for users to understand and display.

[0722] "Means of recognizing emotional states and adjusting feedback accordingly" refers to a process that analyzes the user's current emotions and provides optimal messages and advice based on those results.

[0723] "Means of providing information analysis and feedback to users through a secure channel" refers to the process of transmitting analysis results and feedback to users using a secure communication protocol.

[0724] This invention is a system designed to promote environmentally friendly behavior, supporting sustainable eco-activities by analyzing user behavior data and providing personalized feedback.

[0725] Users input their daily eco-friendly activities using mobile or computer devices. This input includes text data such as, for example, "I used my bicycle when I went out today." The device sends the input data to a server. This communication typically uses an internet connection.

[0726] The server uses data analysis libraries (e.g., Pandas, NumPy) to analyze the received behavioral data. This analysis quantifies the impact of the user's eco-friendly behavior on the environment. For example, it may be expressed as a reduction in CO2 emissions or energy consumption.

[0727] Next, the server uses a generative AI model to simulate future environmental states based on user behavior. This process employs machine learning frameworks (e.g., TensorFlow, PyTorch), and the simulated data is transformed into a visual format. This allows users to visually understand how their actions will affect the environment in the future.

[0728] Furthermore, the server is equipped with an emotion engine that uses natural language processing technology to infer the user's emotional state. Based on the user's text input and past behavioral data, it recognizes the user's current emotions and generates corresponding feedback messages. If the user is experiencing positive emotions, a message encouraging more challenging eco-friendly behaviors is generated. On the other hand, if negative emotions are detected, a message emphasizing encouragement and the importance of action is generated.

[0729] Examples of prompts include: "Suggest a feedback message to send when the user is feeling positive emotions," and "Generate specific advice to help the user continue eco-friendly behavior."

[0730] In this way, users can understand the specific impact of their actions and receive personalized feedback, which is expected to promote sustainable eco-friendly activities.

[0731] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0732] Step 1:

[0733] Users input their environmental improvement actions using mobile or computer terminals. Specifically, they input their eco-friendly activities (e.g., "I commuted by bicycle") in text format into the terminal. The terminal checks the integrity of this input data, converts it to the correct format, and then sends it to the server. The input data contains information about observed eco-friendly activities, and the output is a notification that the transmission to the server is complete.

[0734] Step 2:

[0735] The server receives behavioral data sent from the terminal. The received data is stored in a database and then processed using a data analysis library (e.g., Pandas). The server then analyzes the content of the behavior and begins calculations to quantify its impact on the environment. The input is the user's eco-behavior information, and the output is a numerical representation of the impact (e.g., CO2 reduction).

[0736] Step 3:

[0737] The server uses a generative AI model based on the quantified impact results to simulate the predicted future environmental state if the user's eco-friendly behavior continues. This generative process utilizes machine learning algorithms (e.g., TensorFlow) to make long-term predictions. The input is the quantified impact results, and the output is numerical data of the predicted environmental state.

[0738] Step 4:

[0739] The server visualizes the simulation results. It converts the generated numerical data into graphs and charts using a graphics library, making it easy for the user to understand. The input is numerical data of the predicted environmental state, and the output is visualized environmental prediction information.

[0740] Step 5:

[0741] The server analyzes the user's emotional state using an emotion engine and generates appropriate feedback. It analyzes text data using natural language processing techniques to recognize the user's current emotions. Inputs include past behavioral data and user text comments, while output is an emotion-based feedback message.

[0742] Step 6:

[0743] The server sends generated feedback messages and visualized environmental forecast information to the terminal via a secure protocol. Users receive this feedback and information on their terminal and use it to understand the results of their eco-friendly actions and to motivate themselves for the next steps. The input is the feedback message and visualized information, and the output is the display on the user's terminal.

[0744] (Application Example 2)

[0745] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0746] There is a need for a feedback system that streamlines users' environmental improvement actions and promotes sustainable eco-activities. This invention aims to maintain user motivation and promote sustainable behavioral change by recognizing the user's emotional state and providing customized feedback accordingly.

[0747] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0748] In this invention, the server includes means for receiving environmental improvement behavior data from the user, means for analyzing the received behavior data and quantifying its impact on the environment, and means for using a recognition engine to identify the emotional state and adjust the feedback according to that emotion. This makes it possible to customize the feedback based on the user's emotional state.

[0749] "Environmental improvement behavior data" refers to information about environmentally friendly actions taken by users, such as records of actions aimed at saving energy or reducing carbon dioxide emissions.

[0750] "Quantification" refers to the process of converting analyzed data into specific numerical values, making it possible to evaluate it quantitatively.

[0751] A "generative algorithm" is a computational procedure or method used to predict future environmental conditions based on the results of data analysis.

[0752] "Visually displaying" means providing generated information in a way that is easy for the user to understand, using a visual medium such as a display.

[0753] "Feedback" refers to evaluations and advice given to users regarding their actions, serving as motivation to promote or improve their future behavior.

[0754] "Emotional state" refers to the mental state or mood a user is experiencing at a particular moment, and it is a factor that influences their behavior.

[0755] A "recognition engine" refers to technology used to analyze a user's emotions and behavior, and is a mechanism for determining the emotions a user is experiencing.

[0756] This system works by collecting environmental improvement behavior data using users' smart devices and computers and sending it to the cloud. When users input their daily eco-friendly actions, this data is sent to the server. The server analyzes this data and quantifies its environmental impact. This quantification includes CO2 reduction and energy consumption.

[0757] Based on the analysis results, the server simulates future environmental conditions using a generated AI model. The generated future environmental conditions are sent to the user's device as visual content. This content is displayed on the user's smart device or computer display in an intuitively easy-to-understand format.

[0758] Furthermore, this system uses an emotion recognition engine to identify the user's emotional state. This is done through facial expression and voice analysis. Based on this emotional state, the system fine-tunes the feedback. If positive emotions are identified, it encourages more challenging eco-activities; if negative emotions are identified, it suggests more realistic and achievable activities. This strengthens the user's motivation to consistently engage in environmental improvement behaviors.

[0759] For example, if a user enters "I commuted by train today," the server analyzes the environmental impact of this action, and if the emotion recognition engine determines that the user has achieved something, it provides a message such as, "Your train commute reduced CO2 emissions by 20 kg. That's great! If you keep it up, you can reach 100 kg per month!" In this example, the feedback is appropriately adjusted to maintain the user's motivation.

[0760] An example of a prompt message would be: "Visually represent the CO2 reduction effect of a user choosing the train as an eco-friendly activity in 100 characters or less. Also, add an encouraging message if you determine that the user has achieved a sense of accomplishment." This is how the input is given to the generating AI model.

[0761] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0762] Step 1:

[0763] Users input data on their daily environmental improvement actions using a smart device or computer. The input data includes information about specific actions (e.g., cycling to work, using reusable items) and is sent to a server.

[0764] Step 2:

[0765] The server analyzes the received behavioral data and quantifies its environmental impact. This quantification includes CO2 reduction and energy consumption reduction effects. Using the received behavioral data as input, a specific transformation algorithm is applied to output concrete environmental indicators. This process measures the extent to which user behavior contributes to the environment.

[0766] Step 3:

[0767] The server uses a generative AI model to simulate future environmental conditions based on the analysis results. It takes quantified environmental impact data as input, the AI ​​performs the simulation, and outputs environmental visual data, including future predictions. This visualizes the long-term environmental contribution.

[0768] Step 4:

[0769] The server uses an emotion recognition engine to identify the user's emotional state. It takes facial expression data and voice data acquired from the device's camera and microphone as input, analyzes it, and outputs the user's emotional state. This reveals what emotions the user is currently experiencing.

[0770] Step 5:

[0771] The server generates feedback based on simulation results and the user's emotional state. Using simulation results and emotional data as input, it generates prompts and outputs customized feedback messages using a generation AI model. The system operates by adjusting the feedback accurately to match the user's emotions.

[0772] Step 6:

[0773] The terminal displays feedback messages and visual content generated by the server to the user. It takes data received from the server as input and outputs it to the screen in a visually easy-to-understand format. This allows the user to visually confirm their environmental improvement actions and motivates them to take further action.

[0774] 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 controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0775] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0776] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the robot 414.

[0777] Furthermore, the emotion identification model 59, acting 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 a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0778] Figure 9 shows an emotion map 400 in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0779] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0780] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0781] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, motorcycles, etc., emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0782] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is 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 the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0783] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0784] The above description primarily focuses on the functions of the data processing device 12 in relation to this disclosure. However, the system related to this disclosure is not necessarily implemented on a server. The system related to this disclosure may be implemented as a general information processing system. This disclosure may be implemented, for example, as a software program that runs on a personal computer or as an application that runs on a smartphone. The method related to this disclosure may be provided to users in SaaS (Software as a Service) format.

[0785] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing of the specific process may be performed by multiple computers, including computer 22. For example, a data generation model 58 may be provided in an external device of the data processing device 12, and the external device may generate data according to the input data.

[0786] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0787] 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.

[0788] Furthermore, it is not necessary to store the entirety 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 the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0789] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0790] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of 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). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0791] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0792] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0793] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and the like that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0794] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted as being incorporated by reference.

[0795] The following is further disclosed regarding the embodiments described above.

[0796] (Claim 1)

[0797] A means of receiving environmental improvement action data from users,

[0798] A means of analyzing received behavioral data and quantifying its impact on the environment,

[0799] A method using a generation algorithm that simulates future environmental conditions based on the analysis results,

[0800] A means of visually displaying the generated future environmental state,

[0801] A means of providing feedback to the user based on the simulation results,

[0802] A system that includes this.

[0803] (Claim 2)

[0804] The system according to claim 1, comprising means that enable a user to participate in an eco-behavior challenge and compare their results with those of other users.

[0805] (Claim 3)

[0806] The system according to claim 1, further comprising means for predicting long-term environmental impacts by accumulating user behavior data.

[0807] "Example 1"

[0808] (Claim 1)

[0809] A means of receiving activity information from users,

[0810] A means of analyzing received activity information and quantifying its impact on the environment,

[0811] A method using a generative model to predict future environmental conditions based on the analysis results,

[0812] A means of visually outputting the generated prediction information,

[0813] A means of reporting to users based on predictive information,

[0814] A system that includes this.

[0815] (Claim 2)

[0816] The system according to claim 1, comprising means that enable users to participate in activity tasks and compare their results with those of other users.

[0817] (Claim 3)

[0818] The system according to claim 1, further comprising means for accumulating user activity information and predicting long-term impacts.

[0819] "Application Example 1"

[0820] (Claim 1)

[0821] A means of receiving environmental improvement action data from users,

[0822] A means of analyzing received behavioral data and quantifying its impact on the environment,

[0823] A method using a generation algorithm that simulates future environmental conditions based on the analysis results,

[0824] A means of visually displaying the generated future environmental state,

[0825] A means of obtaining product identification information and obtaining environmental impact information from a product characteristics database,

[0826] A means of providing users with environmental impact information and feedback on their eco-footprint,

[0827] A system that includes this.

[0828] (Claim 2)

[0829] The system according to claim 1, comprising means that enable a user to participate in an eco-behavior challenge and compare their results with those of other users.

[0830] (Claim 3)

[0831] The system according to claim 1, further comprising means for predicting long-term environmental impacts by accumulating user behavior data.

[0832] "Example 2 of combining an emotion engine"

[0833] (Claim 1)

[0834] A means of receiving information on environmental improvement actions from users,

[0835] A means of analyzing received behavioral information and quantifying its impact on the environment,

[0836] A method that uses a generation method to simulate future environmental conditions based on the analysis results,

[0837] A means of visually transforming and outputting the generated future environmental state,

[0838] A means of recognizing the user's emotional state and adjusting the feedback accordingly,

[0839] A means of providing information analysis and feedback to users through a secure channel,

[0840] A system that includes this.

[0841] (Claim 2)

[0842] The system according to claim 1, comprising means for enabling users to participate in environmental action challenges and compare their results with those of other users.

[0843] (Claim 3)

[0844] The system according to claim 1, further comprising means for predicting long-term environmental impacts by accumulating user behavior information.

[0845] "Application example 2 when combining with an emotional engine"

[0846] (Claim 1)

[0847] A means of receiving environmental improvement action data from users,

[0848] A means of analyzing received behavioral data and quantifying its impact on the environment,

[0849] A method using a generation algorithm that simulates future environmental conditions based on the analysis results,

[0850] A means of visually displaying the generated future environmental state,

[0851] A means of providing feedback to the user based on the simulation results,

[0852] A means of using a recognition engine to identify emotional states and adjust feedback according to those emotions,

[0853] A system that includes this.

[0854] (Claim 2)

[0855] The system according to claim 1, comprising means that enable a user to participate in an eco-behavior challenge and compare their results with those of other users.

[0856] (Claim 3)

[0857] The system according to claim 1, further comprising means for predicting long-term environmental impacts by accumulating user behavior data. [Explanation of Symbols]

[0858] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

1. A means of receiving environmental improvement action data from users, A means of analyzing received behavioral data and quantifying its impact on the environment, A method using a generation algorithm that simulates future environmental conditions based on the analysis results, A means of visually displaying the generated future environmental state, A means of providing feedback to the user based on the simulation results, A system that includes this.

2. The system according to claim 1, comprising means that enable a user to participate in an eco-behavior challenge and compare their results with those of other users.

3. The system according to claim 1, further comprising means for predicting long-term environmental impacts by accumulating user behavior data.

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