system

Abstract

We provide the system. [Solution] A means for collecting user behavior data, A means of analyzing user behavior patterns and interests using a generation algorithm, Based on the analysis results, a means to propose individualized environmental protection actions, A means of tracking user behavior and awarding points based on a reward system, A means of providing users with visual feedback, A device 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 method for controlling a persona chatbot, which is performed by at least one processor, the method including the steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a character of the chatbot, 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] [[ID=XXX]] In modern society, there is a demand for individuals to change their behaviors towards environmental protection, but there is a problem that it is difficult to continuously perform specific behaviors. It is difficult to maintain motivation because it is difficult to find appropriate behaviors according to individual lifestyles and interests, and it is difficult to feel the contribution to the environment by one's own actions. Furthermore, since conventional environmental activities are a general approach and lack corresponding measures suitable for individual characteristics, there is a problem that it is difficult to achieve long-term behavior change.

Means for Solving the Problems

[0005] This invention comprises means for collecting user behavior data and means for analyzing user behavior patterns and interests using a generation algorithm. By including means for proposing personalized environmental protection actions suitable for the user based on the analysis results, users can take concrete actions that are in line with their lifestyle. Furthermore, motivation is increased by tracking the user's behavior and awarding points based on a reward system. In addition, by providing users with visual feedback, the invention provides means for them to realize the impact their actions have on the environment and promote sustainable behavioral change.

[0006] "User behavior data" refers to information about a user's actions, habits, and behaviors in their daily life, including their means of transportation and consumption habits.

[0007] A "generative algorithm" refers to a method that analyzes patterns based on input data and automatically generates personalized suggestions for the user.

[0008] "Behavioral patterns" refer to the tendencies of a series of actions and habits in a user's daily life, and are characteristics extracted from the user's statements and behavioral history.

[0009] "Personalized environmental protection actions" refer to specific activities and initiatives for sustainable environmental considerations that are proposed based on analysis results and tailored to individual users.

[0010] A "reward system" refers to a mechanism that provides users with motivation by awarding points or rewards for specific actions or achievements.

[0011] "Feedback" refers to information and evaluations provided to users regarding their actions, serving as a means to reflect the impact of their own activities and encourage further behavioral improvement. [Brief explanation of the drawing]

[0012] [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] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Figure 11] This is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] This is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] This is a sequence diagram showing the processing flow of the data processing system in Example 2, which incorporates an emotion engine. [Figure 14] This is a sequence diagram showing the processing flow of the data processing system in Application Example 2, which combines an emotion engine. [Modes for carrying out the invention]

[0013] Hereinafter, an example of an embodiment of the system relating to the technology of this disclosure will be described with reference to the attached drawings.

[0014] First, the terms used in the following description will be explained.

[0015] In the following embodiments, the labeled processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Also, the processor may be a single type of arithmetic unit or a combination of multiple 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.

[0016] In the following embodiments, the labeled RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor.

[0017] In the following embodiments, the labeled storage is one or more non-volatile storage devices that store various programs, various parameters, and the like. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes, and the like.

[0018] In the following embodiments, the labeled communication I / F (Interface) is an interface including a communication processor, an antenna, and the like. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0019] 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."

[0020] [First Embodiment]

[0021] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0022] 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.

[0023] 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).

[0024] 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.

[0025] 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.

[0026] 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.

[0027] 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.

[0028] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0029] 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.

[0030] 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.

[0031] 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.

[0032] 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".

[0033] This invention is a system for promoting user behavioral change and advancing sustainable environmental protection activities. This system collects user behavioral data and analyzes it using a generation algorithm to present personalized environmental protection behaviors. Furthermore, it promotes sustained behavioral change by providing users with points through a reward system and visual feedback.

[0034] In implementing the system, terminals collect daily behavioral data through smartphones and wearable devices with the user's permission. This data includes transportation methods, consumption activities, and location information. This data is sent to a server, which uses a generation algorithm to analyze the user's behavioral patterns and interests. Based on the analysis results, the server generates a list of environmentally friendly behaviors optimized for the user.

[0035] The presented list of actions is displayed on the device, allowing users to select actions that suit their interests and lifestyle. The status of selected actions is tracked by the device and reported to the server. The server operates a reward system based on this information, calculating and awarding points to users as they complete their actions. These points are visually displayed to the user on the device, serving as feedback to help them concretely understand their level of achievement and contribution to the environment.

[0036] For example, if a user selects the action "participate in a local cleanup activity on the weekend," the device tracks the achievement of the action through the user's location information and event participation confirmation. Based on this information, the server awards the user appropriate points and displays their contribution to the eco-friendly activity. In this way, the system can provide users with specific and personalized feedback, naturally promoting sustainable behavior.

[0037] The following describes the processing flow.

[0038] Step 1:

[0039] The device collects data on the user's daily activities. It obtains data such as steps taken, location information, mode of transportation, and energy consumption from smartphones and wearable devices. This data is collected only with the user's permission.

[0040] Step 2:

[0041] The terminal sends the collected data to the server. It uses a communication protocol to securely transmit the data, allowing the server to prepare for data analysis.

[0042] Step 3:

[0043] The server analyzes the received data using a generation algorithm. It extracts user behavior patterns and trends, and identifies recommended environmental protection actions by referring to past data and the behavior history of similar users.

[0044] Step 4:

[0045] Based on the analysis results, the server generates a personalized list of environmental protection actions suitable for the user. This list includes the difficulty level of achievement and the expected CO2 reduction effect.

[0046] Step 5:

[0047] The device notifies the user of a generated list of environmental protection actions. The user can review the suggested actions through the device and select actions that suit their interests and lifestyle.

[0048] Step 6:

[0049] The user performs the suggested actions. Based on the selected actions, they begin taking specific actions in their daily life.

[0050] Step 7:

[0051] The device monitors the user's actions and reports progress to the server. It uses location information and sensor data to confirm the completion of actions and sends relevant data to the server.

[0052] Step 8:

[0053] The server calculates and awards points based on the user's performance using a reward system. Points are calculated as rewards according to the type of action and the degree of achievement.

[0054] Step 9:

[0055] The server generates feedback on the user's actions and sends it to the device along with data visualizing the environmental impact. The device then displays this information to the user, visually communicating the concrete results of their eco-friendly activities.

[0056] Step 10:

[0057] The device supports sustained efforts by presenting newly suggested actions and rewards to users who have received feedback, encouraging them to take on further challenges.

[0058] (Example 1)

[0059] 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."

[0060] Existing systems designed to promote environmental protection activities face the challenge of not effectively providing sustained motivation and feedback on user behavior. Furthermore, they fail to adequately address users' interests and lifestyles because they offer only general recommendations and cannot suggest specific actions optimized for individual users.

[0061] 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.

[0062] In this invention, the server includes means for collecting information about user behavior, means for analyzing user behavioral characteristics and interests using a generative AI model, and means for providing guidance to the user as visual information. This enables sustained motivation through personalized recommendations and rewards for environmentally friendly behaviors for the user.

[0063] "Means of collecting information about user behavior" refers to devices and methods for collecting data on users' daily activities, and includes technologies that utilize smartphones, wearable devices, and other similar tools.

[0064] A "generative AI model" is an artificial intelligence system that uses machine learning algorithms to learn specific patterns and trends from data and generate suggestions based on the user's behavioral characteristics and interests.

[0065] "Means for analyzing user behavioral characteristics and interests" refers to technologies that analyze information about a user's interests and lifestyle based on collected user data, and reveal their behavioral patterns.

[0066] A "means for recommending customized environmental protection actions" is a mechanism for suggesting personalized and specific environmental protection actions to users based on analyzed user data.

[0067] "Means for calculating rewards based on a redemption system" refers to a system that evaluates the user's level of achievement and calculates appropriate points or rewards accordingly.

[0068] "Means of providing guidance to users as visual information" refers to display methods and devices that allow users to visually confirm their level of achievement and progress, and usually refers to technologies that provide feedback through displays or similar means.

[0069] This invention relates to a system that promotes users' environmental protection activities. This system can recommend optimal actions for individual users by collecting user behavior data and analyzing it using a generative AI model. Furthermore, it encourages sustained behavioral change by awarding users points through a reward system and providing visual feedback.

[0070] The device collects behavioral information through smartphones and wearable devices with the user's permission. This information includes location data, means of transportation, and consumption activity logs. The collected data is encrypted end-to-end and transmitted to the server over the network.

[0071] The server processes the received data using a generating AI model to analyze the user's behavior patterns and interests. This AI model is based on machine learning algorithms and specifically performs big data processing to evaluate the user's past behavior and interests. An example of a prompt might be, "Based on this data, suggest environmentally friendly actions suitable for the user."

[0072] Based on the analysis results, the server generates a user-specific list of environmental protection actions. This list is sent to the device and displayed to the user. The user selects and performs actions that match their interests from this list. The device tracks the user's selected actions and reports the achievement status to the server based on location information and behavioral data.

[0073] The server operates a reward system based on the reported information, calculating and awarding points according to the user's actions. The terminal then visually displays these evaluation results to the user, allowing the user to check their contribution in real time.

[0074] This system enables information sharing and communication among users and also functions as a foundation for encouraging mutually sustainable behavior.

[0075] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0076] Step 1:

[0077] The device records the user's daily activities. This process is carried out using smartphones and wearable devices, and specifically includes GPS data, movement data from accelerometers, and application usage history. This data is provided to the system as input information indicating the user's current location and usage patterns.

[0078] Step 2:

[0079] The device sends the collected behavioral data to the server. This data transmission is end-to-end encrypted using a communication protocol. The input is the user's behavioral data, and the output is a data packet arranged for the server to receive.

[0080] Step 3:

[0081] The server begins analyzing the received data. The data is input into the generating AI model, and the prompt "Based on this data, suggest environmentally friendly actions suitable for the user" is used. The AI ​​model analyzes behavioral patterns using the input dataset and suggests specific environmentally friendly actions. Through this analysis process, recommended actions based on the user's interests and behavioral trends are obtained as output.

[0082] Step 4:

[0083] The server sends a list of recommended actions to the terminal. The input is the recommended actions from the generating AI, and the output is to send this information to the terminal in the appropriate format.

[0084] Step 5:

[0085] The terminal presents the user with recommended actions received from the server. The user can select an action that suits their interests from the displayed list. Here, the user's selection becomes the input, and the selection result becomes the output for the next process.

[0086] Step 6:

[0087] The user then performs the selected action. For example, this could be participating in a local cleanup activity on the weekend. As a result, information about the performed action is stored as input on the device.

[0088] Step 7:

[0089] The device tracks the user's actions and reports details of those actions to the server. The input is a log of the actions performed, and the output is the collected data sent to the server.

[0090] Step 8:

[0091] The server calculates reward points based on the user's performance. The input is user performance data, and the server outputs a specific number of points through data calculations.

[0092] Step 9:

[0093] The server sends the calculated points to the terminal, which then presents this information to the user as visual data. The user can then check their contribution and achievements to inform their next actions. The input is point information from the server, and the output is feedback showing the evaluation to the user.

[0094] (Application Example 1)

[0095] 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."

[0096] In modern society, promoting sustainable environmental conservation activities is a crucial issue, but it is not easy for individual consumers to concretely understand the environmental impact of their own actions and change their behavior. Furthermore, conventional methods lack sufficient promotion of environmental activities directly linked to purchasing behavior, and mechanisms to provide clear incentives to consumers are inadequate. Therefore, there is a need to provide a system that enables consumers to make environmentally friendly choices voluntarily and sustainably.

[0097] 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.

[0098] In this invention, the server includes means for collecting user behavior information, means for analyzing user behavioral trends and interests using a generation algorithm, and means for presenting personalized environmental conservation actions based on the analysis results. This makes it possible to analyze a user's purchasing behavior at physical stores and present personalized eco-recommended products. Furthermore, by calculating points after the purchase of a suggested product and displaying visual feedback, a clear incentive can be provided to the user, promoting sustainable behavior.

[0099] "User behavior information" refers to data related to the actions users take in their daily lives, such as consumption activities and travel patterns.

[0100] A "generative algorithm" refers to a process that analyzes user behavior and interests based on collected data to provide personalized recommendations.

[0101] "Environmental conservation actions" refer to actions taken by individuals with the aim of protecting the environment or using it sustainably, and include purchasing eco-friendly products and participating in recycling activities.

[0102] "Reward evaluation" refers to an evaluation criterion for providing incentives for actions taken by users, and involves calculating points based on the degree of action completion.

[0103] "Visually presenting" refers to a method of displaying information graphically through a user interface, enabling users to understand it intuitively.

[0104] A "sales facility" refers to a place where consumers purchase goods or services, and includes physical stores and online stores.

[0105] "Eco-recommended products" are products that have environmentally friendly characteristics and are individually recommended based on user behavior data.

[0106] "Communication methods" refer to methods that enable information exchange between users, and include messaging functions using digital devices and networks.

[0107] This invention is a system for promoting sustainable environmental conservation activities. By collecting and analyzing user behavior information, it proposes personalized eco-activities and provides incentives through reward evaluations.

[0108] In this system, terminals collect user behavior information and transmit it to a server. Specifically, smartphones and wearable devices are used to acquire data such as modes of transportation, consumption behavior, and location information. This data is aggregated on the server and analyzed using a generative AI model. The algorithms used for analysis are implemented in programming languages ​​such as Python, and cloud computing services such as AWS® Lambda are utilized.

[0109] Based on the analysis results, the server suggests personalized environmental conservation actions and eco-friendly products to the user. This suggested information is sent to the user's device via push notifications, allowing the user to choose actions based on these suggestions.

[0110] When a user performs a recommended action, such as using an eco-bag to purchase a specific item at a store, the terminal communicates with the store's POS system to report the purchase information to the server. The server then operates a rewards system and awards points to the user. These points are stored in a database such as Firebase and displayed as visual feedback on the user's screen.

[0111] For example, if a user purchases locally produced goods at a food market and uses a coupon, the system will provide feedback stating, "Your local contribution has increased by 20 points." This allows users to instantly understand how their actions contribute to the environment.

[0112] An example of a prompt message is, "Think back to a day when you took an environmentally friendly action and freely express how you felt about it. Consider the impact that action had on you and others." This prompt is expected to encourage users to reflect on their environmental impact and increase their motivation for behavioral change.

[0113] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0114] Step 1:

[0115] The device collects user behavior information. Specifically, it uses smartphones and wearable devices to acquire information on mode of transport, consumption behavior, and location. This data is detected by sensors and applications and transmitted to a server via the internet. The input is the motion data acquired by the sensors, and the output is the data reported to the server.

[0116] Step 2:

[0117] The server analyzes the behavioral information it receives. Using a generative AI model, the server analyzes the user's behavioral tendencies and interests, and generates personalized action suggestions using an algorithm. The input is user data, and the output is personalized activity suggestions based on the analysis results. In this process, Python scripts and cloud services are used to filter the data.

[0118] Step 3:

[0119] The server sends suggestions to the terminal based on the analysis results. The user receives recommended products and action suggestions via push notifications. The input is the analysis results, and the output is the suggestion message sent via push notification.

[0120] Step 4:

[0121] The user reviews the suggested actions on their device and selects to take them. For example, when exchanging a recommended eco-friendly product, the user interface enables the action. The input is the recommendation message, and the output is the user's selection information.

[0122] Step 5:

[0123] The terminal monitors the user's actions and reports purchase information to the server in conjunction with the POS system. Specifically, it confirms the purchase item information and the completion of the event. The input is user actions, and the output is report data on the status of the actions.

[0124] Step 6:

[0125] The server evaluates the performance data, calculates points, and awards them to the user. The points are stored in a database such as Firebase. The input is performance report data, and the output is point information.

[0126] Step 7:

[0127] Finally, the server generates visual feedback and displays it to the user via the terminal. The user can then see the environmental contribution of their actions. The input is point information, and the output is visual feedback. During this process, the database and GUI are updated.

[0128] 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.

[0129] This invention is a system designed to support users in making sustainable behavioral changes, and it incorporates an emotion engine. This system collects user behavioral data and analyzes it using a generation algorithm to propose personalized environmental protection behaviors. The emotion engine then recognizes the user's emotions, enabling adjustments to the content and timing of suggestions, optimization of the reward system, and optimization of feedback.

[0130] The device collects data about the user's daily activities via smartphones and wearable devices. It also uses sensor devices such as cameras and microphones to collect data that infers emotions from the user's facial expressions and voice tone. This data is transmitted to a server.

[0131] The server analyzes the collected behavioral and emotional data using generation algorithms. Behavioral data is used to extract the user's daily behavioral patterns and interests, and to identify appropriate environmental protection actions. Emotional data, on the other hand, is used to determine the user's emotional state, and the content and timing of suggestions are adjusted based on this.

[0132] Based on the analysis results, the server generates a list of environmentally friendly actions best suited to the user. The suggestions are modified according to the user's emotional state, as recognized by the emotion engine, creating an environment where the user is more likely to take action. For example, if the user is tired, easily achievable actions are suggested; if they are feeling more positive, more challenging actions are suggested.

[0133] The device notifies the user of generated suggestions. As the user performs a selected suggestion, the device continuously tracks their emotional state and reports the situation to the server. Based on the degree of action completion and emotions, the server adjusts the reward system and optimizes points and rewards for the user. For example, actions that the user finds pleasing are given more points to increase motivation.

[0134] Furthermore, the server generates feedback based on emotional data and provides it visually through the terminal. For example, when a user takes an appropriate action, it provides a positive message that takes their emotional state into account, supporting continued engagement. This system makes it possible to guide users to optimal environmental protection actions tailored to their emotional state and behavioral characteristics, thereby promoting sustainable behavioral change.

[0135] The following describes the processing flow.

[0136] Step 1:

[0137] The device collects data on the user's daily activities through smartphones and wearable devices. This includes data on modes of transportation, activity time, and consumption behavior. The device also uses cameras and microphones to capture emotional data in real time, such as the user's facial expressions and voice tone.

[0138] Step 2:

[0139] The device sends collected behavioral and emotional data to the server. The data is encrypted during transmission and stored in an analysis database.

[0140] Step 3:

[0141] The server uses a generation algorithm to analyze behavioral patterns and user interests based on the received data. In particular, it identifies patterns such as consistent user interests and daily activities.

[0142] Step 4:

[0143] The server uses an emotion engine to analyze emotional data and determine the user's current emotional state. This includes emotional elements such as happiness, stress levels, and fatigue.

[0144] Step 5:

[0145] The server integrates the analysis results and proposes personalized environmental protection actions tailored to the user's emotional state. For example, if the server determines that the user is tired, it will suggest minor eco-friendly activities.

[0146] Step 6:

[0147] The device notifies the user of a list of generated environmental protection actions. The user reviews the suggested actions through the device and selects which actions to take.

[0148] Step 7:

[0149] The user performs the selected action. The device continues to monitor the user's emotional state to see if it affects the ongoing activity.

[0150] Step 8:

[0151] The device tracks the user's actions and reports progress data to the server. The degree of action completion and real-time emotional state are uploaded to the server.

[0152] Step 9:

[0153] The server operates a reward system based on the degree of activity completion and emotional state, awarding appropriate points to the user. For example, if a user completes an activity while experiencing positive emotions, they will receive additional points.

[0154] Step 10:

[0155] The server generates feedback that takes emotional data into account and provides it to the user through the terminal. The feedback highlights the environmental impact of the achieved actions and the positive emotional changes, encouraging the user to promote sustainable behavior.

[0156] (Example 2)

[0157] 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".

[0158] In modern society, it is important to sustainably promote individual environmental protection activities, but there is a lack of mechanisms to propose specific actions tailored to each individual and to support their implementation. Traditional methods have the challenge of not being able to provide appropriate suggestions and feedback that take into account the user's emotions and behavioral characteristics, and thus failing to maintain motivation.

[0159] 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.

[0160] In this invention, the server includes means for acquiring user behavior-related information, means for analyzing user behavior patterns and interests using computational algorithms, and means for analyzing user emotional data and dynamically adjusting the content and timing of suggestions. This makes it possible to individually propose the most suitable environmental protection activities for each user, thereby promoting sustainable behavioral change in users.

[0161] "User behavior-related information" refers to data about the user's daily activities, including location information, step count, and applications used.

[0162] A "computational algorithm" refers to a computational method used to analyze digital data and extract specific patterns or points of interest.

[0163] "Emotional data" refers to data acquired to understand a user's emotional state, and includes physiological indicators such as facial expressions, tone of voice, and heart rate.

[0164] "Means of dynamic adjustment" refers to a function that changes the content and timing of suggestions in real time in response to changes in the user's emotions and behavior.

[0165] "Environmental protection activities" refer to specific actions aimed at protecting or improving the natural environment, and include activities such as recycling, tree planting, and energy conservation.

[0166] This invention is a system that supports sustainable behavioral change in users and incorporates an emotion processing engine. The system uses technology to collect and analyze data on user behavior and emotions using multiple hardware and software components.

[0167] Hardware configuration:

[0168] The device acquires user behavior-related information via smartphones and wearable devices. Specifically, it utilizes data from the smartphone's built-in location sensor, pedometer, and applications being used. It also uses sensors such as cameras and microphones to acquire emotional data from the user's facial expressions and voice.

[0169] Software configuration:

[0170] The server processes the received data and uses a generative AI model to analyze the user's behavioral patterns and emotional state. This model generates suggestions based on behavioral and emotional data and dynamically adjusts their content. The server then recommends the most appropriate environmental protection activities for the user.

[0171] Specific example:

[0172] For example, if data shows a user has previously shown interest in recycling activities, the server will send a specific suggestion to the device, such as, "Why not participate in a local recycling event this weekend?" The device will then present this suggestion to the user as a notification, assisting them in making an action decision.

[0173] Prompt example:

[0174] "User behavioral data: Recycling participation history; emotional data: Detect positive emotional states. Based on this, generate the next environmental protection action to be taken."

[0175] This system makes it possible to encourage optimal and sustainable behavioral change tailored to the user's emotional state and behavioral characteristics.

[0176] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0177] Step 1:

[0178] The device collects data about the user's daily activities through smartphones and wearable devices. Specifically, it acquires location information, step count, and application usage history, and infers emotions from facial expressions and voice using the camera and microphone. This data is transmitted from the device to a server. The input consists of user data and emotion data, and the output is the data transmitted to the server.

[0179] Step 2:

[0180] The server uses a generative AI model to analyze the received behavioral and emotional data. It extracts user behavior patterns and interests from the behavioral data and evaluates the user's current emotional state from the emotional data. The input is data transmitted from the terminal, and the output is the basis for behavioral suggestions resulting from the analysis.

[0181] Step 3:

[0182] Based on the analysis results, the server generates the most suitable environmental protection activities for the user. The generating AI model outputs specific suggestions tailored to the user's characteristics and emotional state. For example, it might recommend participation in recycling activities. In this step, the analysis results are the input, and the output is the suggested content.

[0183] Step 4:

[0184] The device notifies the user of suggestions generated by the server. The user then decides which activity to perform based on these suggestions. Notifications are sent via smartphone push notifications or in-app messages. The input is the suggestion content, and the output is the notification sent to the user.

[0185] Step 5:

[0186] The user performs a selected activity, and throughout this process, the device continuously monitors the user's behavior and emotional state. The device reports this data to the server, recording the details of the activity's execution. The input is the user's activity data, and the output is the data reported to the server.

[0187] Step 6:

[0188] The server evaluates user behavior based on reported data and optimizes the reward system. This includes generating incentives and feedback that take into account achievement and emotional state. For example, a message such as "Your actions are a great contribution" might be generated. The input is activity evaluation data, and the output is the reward and feedback content.

[0189] (Application Example 2)

[0190] 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."

[0191] In modern society, while there is growing concern about environmental protection, there is a problem of a lack of concrete and effective support for individuals to practice sustainable behavior. Furthermore, because there are not enough means to encourage eco-friendly choices when shopping in physical stores, consumers do not perceive its importance, and sustainable behavior does not take root.

[0192] 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.

[0193] In this invention, the server includes means for collecting user behavior data, means for analyzing user behavior patterns and interests using a generation algorithm, and means for recognizing the user's emotional state and adaptively adjusting the content and timing of suggestions. This makes it possible to propose personalized eco-friendly behaviors in physical stores to users, increasing ease of implementation and satisfaction.

[0194] "Behavioral data" refers to information about users' daily activities and is useful for suggesting environmentally friendly behaviors.

[0195] A "generative algorithm" is a mathematical method used to analyze user behavior patterns and interests.

[0196] "Emotional state" refers to the psychological state inferred from the user's facial expressions and tone of voice, and is information used to adjust suggestions.

[0197] A "reward system" is a mechanism that awards points or rewards to users based on their actions, and is used to encourage sustainable behavior.

[0198] "Feedback" is information that visually communicates the results of suggestions or actions to the user, and is intended to motivate them to take further action.

[0199] A "physical store" is a facility that provides goods and services in a physical space, where customers visit and make purchases in person.

[0200] "Eco-friendly behavior" refers to all environmentally friendly choices and activities that contribute to the realization of a sustainable society.

[0201] This invention is a system that helps users easily practice eco-friendly behavior in physical stores. The system includes the following configuration:

[0202] First, the device collects data on the user's daily activities. Specifically, it uses smartphones and wearable devices to record the user's movements and behavioral patterns when they visit physical stores. This data is transmitted to the server in real time.

[0203] Next, the server uses a generative AI model to analyze the collected behavioral data. Using mathematical methods suited to this purpose, it extracts user behavior patterns and interests and proposes personalized, eco-friendly actions. It also collects emotional data using sensor devices such as cameras and microphones to recognize the user's emotional state in real time.

[0204] Based on this emotional state, the server dynamically adjusts the content and timing of the suggested actions. For example, it suggests proactive actions when the user is relaxed and simple actions when the user is stressed.

[0205] Furthermore, if a user takes an eco-friendly action suggested in a physical store, the terminal tracks the action and reports it to the server. Based on this information, the server awards points to the user according to a reward system and provides visual feedback through the terminal.

[0206] As a concrete example, when a user is shopping at a physical store, a smartphone app notifies them that "you can earn points by using an eco-bag." When the user uses the eco-bag, the server confirms the action and automatically awards the points.

[0207] An example of a prompt for a generative AI model is: "If the user's emotion is 'relaxed,' how should we suggest positive, eco-friendly actions?"

[0208] This allows users to easily incorporate sustainable behaviors into their habits without any burden.

[0209] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0210] Step 1:

[0211] The device collects the user's daily activity data in real time through smartphones and wearable devices. This data includes location information, distance traveled, and interaction history. Based on this information, the device extracts the dataset necessary for suggesting environmentally friendly behaviors and sends it to the server.

[0212] Step 2:

[0213] The server begins analyzing the received behavioral data using a generative AI model. Through algorithms, the server performs pattern recognition to identify user behavior patterns and interests. In this analysis, the generative AI model re-evaluates each data point and creates a list of individually optimized eco-friendly behaviors.

[0214] Step 3:

[0215] The device's sensor devices (camera, microphone, etc.) record the user's facial expressions and voice, and send this data to a server. The server analyzes this emotion data to infer the user's emotional state. This emotion analysis uses emotion recognition algorithms that evaluate changes in voice tone and facial expressions.

[0216] Step 4:

[0217] The server dynamically adjusts the content and timing of suggested actions based on the user's emotional state. At this stage, it prioritizes the list of actions according to the emotional state and selects the appropriate one. In this process, it may use a prompt message to the generating AI model such as, "If the user's emotion is 'relaxed,' what positive eco-friendly actions should be suggested?"

[0218] Step 5:

[0219] The suggested eco-friendly actions are notified to the user via the device. The user receives the notification through the application and is encouraged to act in accordance with the suggestion. These actions include using reusable shopping bags and purchasing certain environmentally friendly products.

[0220] Step 6:

[0221] If the user performs the suggested action, the device continues to track its execution and reports the execution data to the server. This report includes details about the type of action and its execution status, which the server uses to confirm the success of the action.

[0222] Step 7:

[0223] After confirming the execution, the server analyzes the user's actions and calculates reward points. It then awards the points to the user and provides visual feedback on the device. This feedback clearly indicates an evaluation of the actions and provides guidance for future actions.

[0224] 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.

[0225] 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.

[0226] 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.

[0227] [Second Embodiment]

[0228] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0229] 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.

[0230] 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).

[0231] 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.

[0232] 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.

[0233] 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).

[0234] 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.

[0235] 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.

[0236] 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.

[0237] 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.

[0238] 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.

[0239] 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".

[0240] This invention is a system for promoting user behavioral change and advancing sustainable environmental protection activities. This system collects user behavioral data and analyzes it using a generation algorithm to present personalized environmental protection behaviors. Furthermore, it promotes sustained behavioral change by providing users with points through a reward system and visual feedback.

[0241] In implementing the system, terminals collect daily behavioral data through smartphones and wearable devices with the user's permission. This data includes transportation methods, consumption activities, and location information. This data is sent to a server, which uses a generation algorithm to analyze the user's behavioral patterns and interests. Based on the analysis results, the server generates a list of environmentally friendly behaviors optimized for the user.

[0242] The presented list of actions is displayed on the device, allowing users to select actions that suit their interests and lifestyle. The status of selected actions is tracked by the device and reported to the server. The server operates a reward system based on this information, calculating and awarding points to users as they complete their actions. These points are visually displayed to the user on the device, serving as feedback to help them concretely understand their level of achievement and contribution to the environment.

[0243] For example, if a user selects the action "participate in a local cleanup activity on the weekend," the device tracks the achievement of the action through the user's location information and event participation confirmation. Based on this information, the server awards the user appropriate points and displays their contribution to eco-friendly activities. In this way, the system can provide users with specific and personalized feedback, naturally promoting sustainable behavior.

[0244] The following describes the processing flow.

[0245] Step 1:

[0246] The device collects data on the user's daily activities. It obtains data such as steps taken, location information, mode of transportation, and energy consumption from smartphones and wearable devices. This data is collected only with the user's permission.

[0247] Step 2:

[0248] The terminal sends the collected data to the server. It uses a communication protocol to securely transmit the data, allowing the server to prepare for data analysis.

[0249] Step 3:

[0250] The server analyzes the received data using a generation algorithm. It extracts user behavior patterns and trends, and identifies recommended environmental protection actions by referring to past data and the behavior history of similar users.

[0251] Step 4:

[0252] Based on the analysis results, the server generates a personalized list of environmental protection actions suitable for the user. This list includes the difficulty level of achievement and the expected CO2 reduction effect.

[0253] Step 5:

[0254] The device notifies the user of a generated list of environmental protection actions. The user can review the suggested actions through the device and select actions that suit their interests and lifestyle.

[0255] Step 6:

[0256] The user performs the suggested actions. Based on the selected actions, they begin taking specific actions in their daily life.

[0257] Step 7:

[0258] The device monitors the user's actions and reports progress to the server. It uses location information and sensor data to confirm the completion of actions and sends relevant data to the server.

[0259] Step 8:

[0260] The server calculates and awards points based on the user's performance using a reward system. Points are calculated as rewards according to the type of action and the degree of achievement.

[0261] Step 9:

[0262] The server generates feedback on the user's actions and sends it to the device along with data visualizing the environmental impact. The device then displays this information to the user, visually communicating the concrete results of their eco-friendly activities.

[0263] Step 10:

[0264] The device supports sustained efforts by presenting newly suggested actions and rewards to users who have received feedback, encouraging them to take on further challenges.

[0265] (Example 1)

[0266] 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."

[0267] Existing systems designed to promote environmental protection activities face the challenge of not effectively providing sustained motivation and feedback on user behavior. Furthermore, they fail to adequately address users' interests and lifestyles because they offer only general recommendations and cannot suggest specific actions optimized for individual users.

[0268] 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.

[0269] In this invention, the server includes means for collecting information about user behavior, means for analyzing user behavioral characteristics and interests using a generative AI model, and means for providing guidance to the user as visual information. This enables sustained motivation through personalized recommendations and rewards for environmentally friendly behaviors for the user.

[0270] "Means of collecting information about user behavior" refers to devices and methods for collecting data on users' daily activities, and includes technologies that utilize smartphones, wearable devices, and other similar tools.

[0271] A "generative AI model" is an artificial intelligence system that uses machine learning algorithms to learn specific patterns and trends from data and generate suggestions based on the user's behavioral characteristics and interests.

[0272] "Means for analyzing user behavioral characteristics and interests" refers to technologies that analyze information about a user's interests and lifestyle based on collected user data, and reveal their behavioral patterns.

[0273] A "means for recommending customized environmental protection actions" is a mechanism for suggesting personalized and specific environmental protection actions to users based on analyzed user data.

[0274] "Means of calculating rewards based on a redemption system" refers to a system that evaluates the user's level of achievement and calculates appropriate points or rewards accordingly.

[0275] "Means of providing guidance to users as visual information" refers to display methods and devices that allow users to visually confirm their level of achievement and progress, and typically refers to technologies that provide feedback through displays or similar means.

[0276] This invention relates to a system that promotes users' environmental protection activities. This system can recommend optimal actions for individual users by collecting user behavior data and analyzing it using a generative AI model. Furthermore, it encourages sustained behavioral change by awarding users points through a reward system and providing visual feedback.

[0277] The device collects behavioral information through smartphones and wearable devices with the user's permission. This information includes location data, means of transportation, and consumption activity logs. The collected data is encrypted end-to-end and transmitted to the server over the network.

[0278] The server processes the received data using a generating AI model to analyze the user's behavior patterns and interests. This AI model is based on machine learning algorithms and specifically performs big data processing to evaluate the user's past behavior and interests. An example of a prompt might be, "Based on this data, suggest environmentally friendly actions suitable for the user."

[0279] Based on the analysis results, the server generates a user-specific list of environmental protection actions. This list is sent to the device and displayed to the user. The user selects and performs actions that match their interests from this list. The device tracks the user's selected actions and reports the achievement status to the server based on location information and behavioral data.

[0280] The server operates a reward system based on the reported information, calculating and awarding points according to the user's actions. The terminal then visually displays these evaluation results to the user, allowing the user to check their contribution in real time.

[0281] This system enables information sharing and communication among users and also functions as a foundation for encouraging mutually sustainable behavior.

[0282] The flow of the specific process in Example 1 will be described using FIG. 11.

[0283] Step 1:

[0284] The terminal records the user's daily activities. This process is carried out using a smartphone or wearable device, and specifically includes GPS data, movement data from an acceleration sensor, the usage history of applications, etc. These data are provided to the system as input information indicating the user's current location and usage patterns.

[0285] Step 2:

[0286] The terminal transmits the collected activity data to the server. End-to-end encryption is performed on this data transmission using a communication protocol. The input is the user's activity data, and the output is the data packet arranged for the server to receive.

[0287] Step 3:

[0288] The server begins to analyze the received data. The data is input into the generative AI model, and the prompt sentence "Please propose environmental protection actions suitable for the user based on this data" is used. The AI model analyzes the behavior pattern using the input data set and proposes specific environmental protection actions. Through this analysis operation, recommended actions based on the user's interests and behavior trends are obtained as output.

[0289] Step 4:

[0290] The server transmits the list of obtained recommended actions to the terminal. The input is the recommended actions from the generative AI, and the output is to transmit this information to the terminal in an appropriate format.

[0291] Step 5:

[0292] The terminal presents the user with recommended actions received from the server. The user can select an action that suits their interests from the displayed list. Here, the user's selection becomes the input, and the selection result becomes the output for the next process.

[0293] Step 6:

[0294] The user then performs the selected action. For example, this could be participating in a local cleanup activity on the weekend. As a result, information about the performed action is stored as input on the device.

[0295] Step 7:

[0296] The device tracks the user's actions and reports details of those actions to the server. The input is a log of the actions performed, and the output is the collected data sent to the server.

[0297] Step 8:

[0298] The server calculates reward points based on the user's performance. The input is user performance data, and the server outputs a specific number of points through data calculations.

[0299] Step 9:

[0300] The server sends the calculated points to the terminal, which then presents this information to the user as visual data. The user can then check their contribution and achievements to inform their next actions. The input is point information from the server, and the output is feedback showing the evaluation to the user.

[0301] (Application Example 1)

[0302] 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."

[0303] In modern society, it is an important issue to promote sustainable environmental protection activities. However, it is not easy for individual consumers to specifically understand the environmental impact of their own actions and transform their behaviors. In addition, conventional methods lack promotion of environmental activities directly related to purchasing behaviors, and the mechanism for providing clear incentives to consumers is also insufficient. Therefore, there is a demand for a system that enables consumers to make spontaneous and sustainable environmentally friendly choices.

[0304] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0305] In this invention, the server includes means for collecting the user's behavior information, means for analyzing the user's behavior tendency and interests using a generation algorithm, and means for presenting individualized environmental protection actions based on the analysis result. As a result, it becomes possible to analyze the user's purchasing behavior in a physical store and present individualized eco-recommended products. Furthermore, by calculating points and visually displaying feedback after the proposed product is purchased, clear incentives can be provided to the user, and sustainable behaviors can be promoted.

[0306] The "user's behavior information" is information related to the actions that the user performs in daily life, and is data including, for example, consumption activities and movement patterns.

[0307] The "generation algorithm" refers to a process for analyzing the user's behavior tendency and interests based on the collected data and making personalized recommendations.

[0308] The "environmental protection action" refers to actions that an individual undertakes for the purpose of protecting the environment and sustainable use, and includes purchasing eco-products and participating in recycling activities.

[0309] The "reward evaluation" is an evaluation criterion for giving incentives to the actions performed by the user, and calculates points according to the action achievement level.

[0310] "Visually presenting" refers to a method of displaying information graphically through a user interface, enabling users to understand it intuitively.

[0311] A "sales facility" refers to a place where consumers purchase goods or services, and includes physical stores and online stores.

[0312] "Eco-recommended products" are products that have environmentally friendly characteristics and are individually recommended based on user behavior data.

[0313] "Communication methods" refer to methods that enable information exchange between users, and include messaging functions using digital devices and networks.

[0314] This invention is a system for promoting sustainable environmental conservation activities. By collecting and analyzing user behavior information, it proposes personalized eco-activities and provides incentives through reward evaluations.

[0315] In this system, terminals collect user behavior information and transmit it to a server. Specifically, smartphones and wearable devices are used to acquire data such as modes of transportation, consumption behavior, and location information. This data is aggregated on the server and analyzed using a generative AI model. The algorithms used for analysis are implemented in programming languages ​​such as Python, and cloud computing services such as AWS Lambda are utilized.

[0316] Based on the analysis results, the server suggests personalized environmental conservation actions and eco-friendly products to the user. This suggested information is sent to the user's device via push notifications, allowing the user to choose actions based on these suggestions.

[0317] When a user performs a recommended action, such as using an eco-bag to purchase a specific item at a store, the terminal communicates with the store's POS system to report the purchase information to the server. The server then operates a rewards system and awards points to the user. These points are stored in a database such as Firebase and displayed as visual feedback on the user's screen.

[0318] For example, if a user purchases locally produced goods at a food market and uses a coupon, the system will provide feedback stating, "Your local contribution has increased by 20 points." This allows users to instantly understand how their actions contribute to the environment.

[0319] An example of a prompt message is, "Think back to a day when you took an environmentally friendly action and freely express how you felt about it. Consider the impact that action had on you and others." This prompt is expected to encourage users to reflect on their environmental impact and increase their motivation for behavioral change.

[0320] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0321] Step 1:

[0322] The device collects user behavior information. Specifically, it uses smartphones and wearable devices to acquire information on mode of transport, consumption behavior, and location. This data is detected by sensors and applications and transmitted to a server via the internet. The input is the motion data acquired by the sensors, and the output is the data reported to the server.

[0323] Step 2:

[0324] The server analyzes the behavioral information it receives. Using a generative AI model, the server analyzes the user's behavioral tendencies and interests, and generates personalized action suggestions using an algorithm. The input is user data, and the output is personalized activity suggestions based on the analysis results. In this process, Python scripts and cloud services are used to filter the data.

[0325] Step 3:

[0326] The server sends suggestions to the terminal based on the analysis results. The user receives recommended products and action suggestions via push notifications. The input is the analysis results, and the output is the suggestion message sent via push notification.

[0327] Step 4:

[0328] The user reviews the suggested actions on their device and chooses to take them. For example, when exchanging a recommended eco-friendly product, the user interface enables the action. The input is the recommendation message, and the output is the user's selection information.

[0329] Step 5:

[0330] The terminal monitors the user's actions and reports purchase information to the server in conjunction with the POS system. Specifically, it confirms the purchase item information and the completion of the event. The input is user actions, and the output is report data on the status of the actions.

[0331] Step 6:

[0332] The server evaluates the performance data, calculates points, and awards them to the user. The points are stored in a database such as Firebase. The input is performance report data, and the output is point information.

[0333] Step 7:

[0334] Finally, the server generates visual feedback and displays it to the user via the terminal. The user can then see the environmental contribution of their actions. The input is point information, and the output is visual feedback. During this process, the database and GUI are updated.

[0335] 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.

[0336] This invention is a system designed to support users in making sustainable behavioral changes, and it incorporates an emotion engine. This system collects user behavioral data and analyzes it using a generation algorithm to propose personalized environmental protection behaviors. The emotion engine then recognizes the user's emotions, enabling adjustments to the content and timing of suggestions, optimization of the reward system, and optimization of feedback.

[0337] The device collects data about the user's daily activities via smartphones and wearable devices. It also uses sensor devices such as cameras and microphones to collect data that infers emotions from the user's facial expressions and voice tone. This data is transmitted to a server.

[0338] The server analyzes the collected behavioral and emotional data using generation algorithms. Behavioral data is used to extract the user's daily behavioral patterns and interests, and to identify appropriate environmental protection actions. Emotional data, on the other hand, is used to determine the user's emotional state, and the content and timing of suggestions are adjusted based on this.

[0339] Based on the analysis results, the server generates a list of environmentally friendly actions best suited to the user. The suggestions are modified according to the user's emotional state, as recognized by the emotion engine, creating an environment where the user is more likely to take action. For example, if the user is tired, easily achievable actions are suggested; if they are feeling more positive, more challenging actions are suggested.

[0340] The device notifies the user of generated suggestions. As the user performs a selected suggestion, the device continuously tracks their emotional state and reports the situation to the server. Based on the degree of action completion and emotions, the server adjusts the reward system and optimizes points and rewards for the user. For example, actions that the user finds pleasing are given more points to increase motivation.

[0341] Furthermore, the server generates feedback based on emotional data and provides it visually through the terminal. For example, when a user takes an appropriate action, it provides a positive message that takes their emotional state into account, supporting continued engagement. This system makes it possible to guide users to optimal environmental protection actions tailored to their emotional state and behavioral characteristics, thereby promoting sustainable behavioral change.

[0342] The following describes the processing flow.

[0343] Step 1:

[0344] The device collects data on the user's daily activities through smartphones and wearable devices. This includes data on modes of transportation, activity time, and consumption behavior. The device also uses cameras and microphones to capture emotional data in real time, such as the user's facial expressions and voice tone.

[0345] Step 2:

[0346] The device sends collected behavioral and emotional data to the server. The data is encrypted during transmission and stored in an analysis database.

[0347] Step 3:

[0348] The server uses a generation algorithm to analyze behavioral patterns and user interests based on the received data. In particular, it identifies patterns such as consistent user interests and daily activities.

[0349] Step 4:

[0350] The server uses an emotion engine to analyze emotional data and determine the user's current emotional state. This includes emotional elements such as happiness, stress levels, and fatigue.

[0351] Step 5:

[0352] The server integrates the analysis results and proposes personalized environmental protection actions tailored to the user's emotional state. For example, if the server determines that the user is tired, it will suggest minor eco-friendly activities.

[0353] Step 6:

[0354] The device notifies the user of a list of generated environmental protection actions. The user reviews the suggested actions through the device and selects which actions to take.

[0355] Step 7:

[0356] The user performs the selected action. The device continues to monitor the user's emotional state to see if it affects the ongoing activity.

[0357] Step 8:

[0358] The device tracks the user's actions and reports progress data to the server. The degree of action completion and real-time emotional state are uploaded to the server.

[0359] Step 9:

[0360] The server operates a reward system based on the degree of activity completion and emotional state, awarding appropriate points to the user. For example, if a user completes an activity while experiencing positive emotions, they will receive additional points.

[0361] Step 10:

[0362] The server generates feedback that takes emotional data into account and provides it to the user through the terminal. The feedback highlights the environmental impact of the achieved actions and the positive emotional changes, encouraging the user to promote sustainable behavior.

[0363] (Example 2)

[0364] 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".

[0365] In modern society, it is important to sustainably promote individual environmental protection activities, but there is a lack of mechanisms to propose specific actions tailored to each individual and to support their implementation. Traditional methods have the challenge of not being able to provide appropriate suggestions and feedback that take into account the user's emotions and behavioral characteristics, and thus failing to maintain motivation.

[0366] 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.

[0367] In this invention, the server includes means for acquiring user behavior-related information, means for analyzing user behavior patterns and interests using computational algorithms, and means for analyzing user emotional data and dynamically adjusting the content and timing of suggestions. This makes it possible to individually propose the most suitable environmental protection activities for each user, thereby promoting sustainable behavioral change in users.

[0368] "User behavior-related information" refers to data about the user's daily activities, including location information, step count, and applications used.

[0369] A "computational algorithm" refers to a computational method used to analyze digital data and extract specific patterns or points of interest.

[0370] "Emotional data" refers to data acquired to understand a user's emotional state, and includes physiological indicators such as facial expressions, tone of voice, and heart rate.

[0371] "Means of dynamic adjustment" refers to a function that changes the content and timing of suggestions in real time in response to changes in the user's emotions and behavior.

[0372] "Environmental protection activities" refer to specific actions aimed at protecting or improving the natural environment, and include activities such as recycling, tree planting, and energy conservation.

[0373] This invention is a system that supports sustainable behavioral change in users and incorporates an emotion processing engine. The system uses technology to collect and analyze data on user behavior and emotions using multiple hardware and software components.

[0374] Hardware configuration:

[0375] The device acquires user behavior-related information via smartphones and wearable devices. Specifically, it utilizes data from the smartphone's built-in location sensor, pedometer, and applications being used. It also uses sensors such as cameras and microphones to acquire emotional data from the user's facial expressions and voice.

[0376] Software configuration:

[0377] The server processes the received data and uses a generative AI model to analyze the user's behavioral patterns and emotional state. This model generates suggestions based on behavioral and emotional data and dynamically adjusts their content. The server then recommends the most appropriate environmental protection activities for the user.

[0378] Specific example:

[0379] For example, if data shows a user has previously shown interest in recycling activities, the server will send a specific suggestion to the device, such as, "Why not participate in a local recycling event this weekend?" The device will then present this suggestion to the user as a notification, assisting them in making an action decision.

[0380] Prompt example:

[0381] "User behavioral data: Recycling participation history; emotional data: Detect positive emotional states. Based on this, generate the next environmental protection action to be taken."

[0382] This system makes it possible to encourage optimal and sustainable behavioral change tailored to the user's emotional state and behavioral characteristics.

[0383] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0384] Step 1:

[0385] The device collects data about the user's daily activities through smartphones and wearable devices. Specifically, it acquires location information, step count, and application usage history, and infers emotions from facial expressions and voice using the camera and microphone. This data is transmitted from the device to a server. The input consists of user data and emotion data, and the output is the data transmitted to the server.

[0386] Step 2:

[0387] The server uses a generative AI model to analyze the received behavioral and emotional data. It extracts user behavior patterns and interests from the behavioral data and evaluates the user's current emotional state from the emotional data. The input is data transmitted from the terminal, and the output is the basis for behavioral suggestions resulting from the analysis.

[0388] Step 3:

[0389] Based on the analysis results, the server generates the most suitable environmental protection activities for the user. The generating AI model outputs specific suggestions tailored to the user's characteristics and emotional state. For example, it might recommend participation in recycling activities. In this step, the analysis results are the input, and the output is the suggested content.

[0390] Step 4:

[0391] The device notifies the user of suggestions generated by the server. The user then decides which activity to perform based on these suggestions. Notifications are sent via smartphone push notifications or in-app messages. The input is the suggestion content, and the output is the notification sent to the user.

[0392] Step 5:

[0393] The user performs a selected activity, and throughout this process, the device continuously monitors the user's behavior and emotional state. The device reports this data to the server, recording the details of the activity's execution. The input is the user's activity data, and the output is the data reported to the server.

[0394] Step 6:

[0395] The server evaluates user behavior based on reported data and optimizes the reward system. This includes generating incentives and feedback that take into account achievement and emotional state. For example, a message such as "Your actions are a great contribution" might be generated. The input is activity evaluation data, and the output is the reward and feedback content.

[0396] (Application Example 2)

[0397] 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."

[0398] In modern society, while there is growing concern about environmental protection, there is a problem of a lack of concrete and effective support for individuals to practice sustainable behavior. Furthermore, because there are not enough means to encourage eco-friendly choices when shopping in physical stores, consumers do not perceive its importance, and sustainable behavior does not take root.

[0399] 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.

[0400] In this invention, the server includes means for collecting user behavior data, means for analyzing user behavior patterns and interests using a generation algorithm, and means for recognizing the user's emotional state and adaptively adjusting the content and timing of suggestions. This makes it possible to propose personalized eco-friendly behaviors in physical stores to users, increasing ease of implementation and satisfaction.

[0401] "Behavioral data" refers to information about users' daily activities and is useful for suggesting environmentally friendly behaviors.

[0402] A "generative algorithm" is a mathematical method used to analyze user behavior patterns and interests.

[0403] "Emotional state" refers to the psychological state inferred from the user's facial expressions and tone of voice, and is information used to adjust suggestions.

[0404] A "reward system" is a mechanism that awards points or rewards to users based on their actions, and is used to encourage sustainable behavior.

[0405] "Feedback" is information that visually communicates the results of suggestions or actions to the user, and is intended to motivate them to take further action.

[0406] A "physical store" is a facility that provides goods and services in a physical space, where customers visit and make purchases in person.

[0407] "Eco-friendly behavior" refers to all environmentally friendly choices and activities that contribute to the realization of a sustainable society.

[0408] This invention is a system that helps users easily practice eco-friendly behavior in physical stores. The system includes the following configuration:

[0409] First, the device collects data on the user's daily activities. Specifically, it uses smartphones and wearable devices to record the user's movements and behavioral patterns when visiting physical stores. This data is transmitted to the server in real time.

[0410] Next, the server uses a generative AI model to analyze the collected behavioral data. Using mathematical methods suited to this purpose, it extracts user behavior patterns and interests and proposes personalized, eco-friendly actions. It also collects emotional data using sensor devices such as cameras and microphones to recognize the user's emotional state in real time.

[0411] Based on this emotional state, the server dynamically adjusts the content and timing of the suggested actions. For example, it suggests proactive actions when the user is relaxed and simple actions when the user is stressed.

[0412] Furthermore, if a user takes an eco-friendly action suggested in a physical store, the terminal tracks the action and reports it to the server. Based on this information, the server awards points to the user according to a reward system and provides visual feedback through the terminal.

[0413] As a concrete example, when a user is shopping at a physical store, a smartphone app notifies them that "you can earn points by using an eco-bag." When the user uses the eco-bag, the server confirms the action and automatically awards the points.

[0414] An example of a prompt for a generative AI model is: "If the user's emotion is 'relaxed,' how should we suggest positive, eco-friendly actions?"

[0415] This allows users to easily incorporate sustainable behaviors into their habits without any burden.

[0416] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0417] Step 1:

[0418] The device collects the user's daily activity data in real time through smartphones and wearable devices. This data includes location information, distance traveled, and interaction history. Based on this information, the device extracts the dataset necessary for suggesting environmentally friendly behaviors and sends it to the server.

[0419] Step 2:

[0420] The server begins analyzing the received behavioral data using a generative AI model. Through algorithms, the server performs pattern recognition to identify user behavior patterns and interests. In this analysis, the generative AI model re-evaluates each data point and creates a list of individually optimized eco-friendly behaviors.

[0421] Step 3:

[0422] The device's sensor devices (camera, microphone, etc.) record the user's facial expressions and voice, and send this data to a server. The server analyzes this emotion data to infer the user's emotional state. This emotion analysis uses emotion recognition algorithms that evaluate changes in voice tone and facial expressions.

[0423] Step 4:

[0424] The server dynamically adjusts the content and timing of suggested actions based on the user's emotional state. At this stage, it prioritizes the list of actions according to the emotional state and selects the appropriate one. In this process, it may use a prompt message to the generating AI model such as, "If the user's emotion is 'relaxed,' what positive eco-friendly actions should be suggested?"

[0425] Step 5:

[0426] The suggested eco-friendly actions are notified to the user via the device. The user receives the notification through the application and is encouraged to act in accordance with the suggestion. These actions include using reusable shopping bags and purchasing certain environmentally friendly products.

[0427] Step 6:

[0428] If the user performs the suggested action, the device continues to track its execution and reports the execution data to the server. This report includes details about the type of action and its execution status, which the server uses to confirm the success of the action.

[0429] Step 7:

[0430] After confirming the execution, the server analyzes the user's actions and calculates reward points. It then awards the points to the user and provides visual feedback on the device. This feedback clearly indicates an evaluation of the actions and provides guidance for future actions.

[0431] 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.

[0432] 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.

[0433] 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.

[0434] [Third Embodiment]

[0435] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0436] 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.

[0437] 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).

[0438] 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.

[0439] 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.

[0440] 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).

[0441] 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.

[0442] 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.

[0443] 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.

[0444] 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.

[0445] 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.

[0446] 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".

[0447] This invention is a system for promoting user behavioral change and advancing sustainable environmental protection activities. This system collects user behavioral data and analyzes it using a generation algorithm to present personalized environmental protection behaviors. Furthermore, it promotes sustained behavioral change by providing users with points through a reward system and visual feedback.

[0448] In implementing the system, terminals collect daily behavioral data through smartphones and wearable devices with the user's permission. This data includes transportation methods, consumption activities, and location information. This data is sent to a server, which uses a generation algorithm to analyze the user's behavioral patterns and interests. Based on the analysis results, the server generates a list of environmentally friendly behaviors optimized for the user.

[0449] The presented list of actions is displayed on the device, allowing users to select actions that suit their interests and lifestyle. The status of selected actions is tracked by the device and reported to the server. The server operates a reward system based on this information, calculating and awarding points to users as they complete their actions. These points are visually displayed to the user on the device, serving as feedback to help them concretely understand their level of achievement and contribution to the environment.

[0450] For example, if a user selects the action "participate in a local cleanup activity on the weekend," the device tracks the achievement of the action through the user's location information and event participation confirmation. Based on this information, the server awards the user appropriate points and displays their contribution to eco-friendly activities. In this way, the system can provide users with specific and personalized feedback, naturally promoting sustainable behavior.

[0451] The following describes the processing flow.

[0452] Step 1:

[0453] The device collects data on the user's daily activities. It obtains data such as steps taken, location information, mode of transportation, and energy consumption from smartphones and wearable devices. This data is collected only with the user's permission.

[0454] Step 2:

[0455] The terminal sends the collected data to the server. It uses a communication protocol to securely transmit the data, allowing the server to prepare for data analysis.

[0456] Step 3:

[0457] The server analyzes the received data using a generation algorithm. It extracts user behavior patterns and trends, and identifies recommended environmental protection actions by referring to past data and the behavior history of similar users.

[0458] Step 4:

[0459] Based on the analysis results, the server generates a personalized list of environmental protection actions suitable for the user. This list includes the difficulty level of achievement and the expected CO2 reduction effect.

[0460] Step 5:

[0461] The device notifies the user of a generated list of environmental protection actions. The user can review the suggested actions through the device and select actions that suit their interests and lifestyle.

[0462] Step 6:

[0463] The user performs the suggested actions. Based on the selected actions, they begin taking specific actions in their daily life.

[0464] Step 7:

[0465] The device monitors the user's actions and reports progress to the server. It uses location information and sensor data to confirm the completion of actions and sends relevant data to the server.

[0466] Step 8:

[0467] The server calculates and awards points based on the user's performance using a reward system. Points are calculated as rewards according to the type of action and the degree of achievement.

[0468] Step 9:

[0469] The server generates feedback on the user's actions and sends it to the device along with data visualizing the environmental impact. The device then displays this information to the user, visually communicating the concrete results of their eco-friendly activities.

[0470] Step 10:

[0471] The device supports sustained efforts by presenting newly suggested actions and rewards to users who have received feedback, encouraging them to take on further challenges.

[0472] (Example 1)

[0473] 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."

[0474] Existing systems designed to promote environmental protection activities face the challenge of not effectively providing sustained motivation and feedback on user behavior. Furthermore, they fail to adequately address users' interests and lifestyles because they offer only general recommendations and cannot suggest specific actions optimized for individual users.

[0475] 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.

[0476] In this invention, the server includes means for collecting information about user behavior, means for analyzing user behavioral characteristics and interests using a generative AI model, and means for providing guidance to the user as visual information. This enables sustained motivation through personalized recommendations and rewards for environmentally friendly behaviors for the user.

[0477] "Means of collecting information about user behavior" refers to devices and methods for collecting data on users' daily activities, and includes technologies that utilize smartphones, wearable devices, and other similar tools.

[0478] A "generative AI model" is an artificial intelligence system that uses machine learning algorithms to learn specific patterns and trends from data and generate suggestions based on the user's behavioral characteristics and interests.

[0479] "Means for analyzing user behavioral characteristics and interests" refers to technologies that analyze information about a user's interests and lifestyle based on collected user data, and reveal their behavioral patterns.

[0480] A "means for recommending customized environmental protection actions" is a mechanism for suggesting personalized and specific environmental protection actions to users based on analyzed user data.

[0481] "Means of calculating rewards based on a redemption system" refers to a system that evaluates the user's level of achievement and calculates appropriate points or rewards accordingly.

[0482] "Means of providing guidance to users as visual information" refers to display methods and devices that allow users to visually confirm their level of achievement and progress, and typically refers to technologies that provide feedback through displays or similar means.

[0483] This invention relates to a system that promotes users' environmental protection activities. This system can recommend optimal actions for individual users by collecting user behavior data and analyzing it using a generative AI model. Furthermore, it encourages sustained behavioral change by awarding users points through a reward system and providing visual feedback.

[0484] The device collects behavioral information through smartphones and wearable devices with the user's permission. This information includes location data, means of transportation, and consumption activity logs. The collected data is encrypted end-to-end and transmitted to the server over the network.

[0485] The server processes the received data using a generating AI model to analyze the user's behavior patterns and interests. This AI model is based on machine learning algorithms and specifically performs big data processing to evaluate the user's past behavior and interests. An example of a prompt might be, "Based on this data, suggest environmentally friendly actions suitable for the user."

[0486] Based on the analysis results, the server generates a user-specific list of environmental protection actions. This list is sent to the device and displayed to the user. The user selects and performs actions that match their interests from this list. The device tracks the user's selected actions and reports the achievement status to the server based on location information and behavioral data.

[0487] The server operates a reward system based on the reported information, calculating and awarding points according to the user's actions. The terminal then visually displays these evaluation results to the user, allowing the user to check their contribution in real time.

[0488] This system enables information sharing and communication among users and also functions as a foundation for encouraging mutually sustainable behavior.

[0489] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0490] Step 1:

[0491] The device records the user's daily activities. This process is carried out using smartphones and wearable devices, and specifically includes GPS data, movement data from accelerometers, and application usage history. This data is provided to the system as input information indicating the user's current location and usage patterns.

[0492] Step 2:

[0493] The device sends the collected behavioral data to the server. This data transmission is end-to-end encrypted using a communication protocol. The input is the user's behavioral data, and the output is a data packet arranged for the server to receive.

[0494] Step 3:

[0495] The server begins analyzing the received data. The data is input into the generating AI model, and the prompt "Based on this data, suggest environmentally friendly actions suitable for the user" is used. The AI ​​model analyzes behavioral patterns using the input dataset and suggests specific environmentally friendly actions. Through this analysis process, recommended actions based on the user's interests and behavioral trends are obtained as output.

[0496] Step 4:

[0497] The server sends a list of recommended actions to the terminal. The input is the recommended actions from the generating AI, and the output is to send this information to the terminal in the appropriate format.

[0498] Step 5:

[0499] The terminal presents the user with recommended actions received from the server. The user can select an action that suits their interests from the displayed list. Here, the user's selection becomes the input, and the selection result becomes the output for the next process.

[0500] Step 6:

[0501] The user then performs the selected action. For example, this could be participating in a local cleanup activity on the weekend. As a result, information about the performed action is stored as input on the device.

[0502] Step 7:

[0503] The device tracks the user's actions and reports details of those actions to the server. The input is a log of the actions performed, and the output is the collected data sent to the server.

[0504] Step 8:

[0505] The server calculates reward points based on the user's performance. The input is user performance data, and the server outputs a specific number of points through data calculations.

[0506] Step 9:

[0507] The server sends the calculated points to the terminal, which then presents this information to the user as visual data. The user can then check their contribution and achievements to inform their next actions. The input is point information from the server, and the output is feedback showing the evaluation to the user.

[0508] (Application Example 1)

[0509] 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."

[0510] In modern society, promoting sustainable environmental conservation activities is a crucial issue, but it is not easy for individual consumers to concretely understand the environmental impact of their own actions and change their behavior. Furthermore, conventional methods lack sufficient promotion of environmental activities directly linked to purchasing behavior, and mechanisms to provide clear incentives to consumers are inadequate. Therefore, there is a need to provide a system that enables consumers to make environmentally friendly choices voluntarily and sustainably.

[0511] 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.

[0512] In this invention, the server includes means for collecting user behavior information, means for analyzing user behavioral trends and interests using a generation algorithm, and means for presenting personalized environmental conservation actions based on the analysis results. This makes it possible to analyze a user's purchasing behavior at physical stores and present personalized eco-recommended products. Furthermore, by calculating points after the purchase of a suggested product and displaying visual feedback, a clear incentive can be provided to the user, promoting sustainable behavior.

[0513] "User behavior information" refers to data related to the actions users take in their daily lives, such as consumption activities and travel patterns.

[0514] A "generative algorithm" refers to a process that analyzes user behavior and interests based on collected data to provide personalized recommendations.

[0515] "Environmental conservation actions" refer to actions taken by individuals with the aim of protecting the environment or using it sustainably, and include purchasing eco-friendly products and participating in recycling activities.

[0516] "Reward evaluation" refers to an evaluation criterion for providing incentives for actions taken by users, and involves calculating points based on the degree of action completion.

[0517] "Visually presenting" refers to a method of displaying information graphically through a user interface, enabling users to understand it intuitively.

[0518] A "sales facility" refers to a place where consumers purchase goods or services, and includes physical stores and online stores.

[0519] "Eco-recommended products" are products that have environmentally friendly characteristics and are individually recommended based on user behavior data.

[0520] "Communication methods" refer to methods that enable information exchange between users, and include messaging functions using digital devices and networks.

[0521] This invention is a system for promoting sustainable environmental conservation activities. By collecting and analyzing user behavior information, it proposes personalized eco-activities and provides incentives through reward evaluations.

[0522] In this system, terminals collect user behavior information and transmit it to a server. Specifically, smartphones and wearable devices are used to acquire data such as modes of transportation, consumption behavior, and location information. This data is aggregated on the server and analyzed using a generative AI model. The algorithms used for analysis are implemented in programming languages ​​such as Python, and cloud computing services such as AWS Lambda are utilized.

[0523] Based on the analysis results, the server suggests personalized environmental conservation actions and eco-friendly products to the user. This suggested information is sent to the user's device via push notifications, allowing the user to choose actions based on these suggestions.

[0524] When a user performs a recommended action, such as using an eco-bag to purchase a specific item at a store, the terminal communicates with the store's POS system to report the purchase information to the server. The server then operates a rewards system and awards points to the user. These points are stored in a database such as Firebase and displayed as visual feedback on the user's screen.

[0525] For example, if a user purchases locally produced goods at a food market and uses a coupon, the system will provide feedback stating, "Your local contribution has increased by 20 points." This allows users to instantly understand how their actions contribute to the environment.

[0526] An example of a prompt message is, "Think back to a day when you took an environmentally friendly action and freely express how you felt about it. Consider the impact that action had on you and others." This prompt is expected to encourage users to reflect on their environmental impact and increase their motivation for behavioral change.

[0527] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0528] Step 1:

[0529] The device collects user behavior information. Specifically, it uses smartphones and wearable devices to acquire information on mode of transport, consumption behavior, and location. This data is detected by sensors and applications and transmitted to a server via the internet. The input is the motion data acquired by the sensors, and the output is the data reported to the server.

[0530] Step 2:

[0531] The server analyzes the behavioral information it receives. Using a generative AI model, the server analyzes the user's behavioral tendencies and interests, and generates personalized action suggestions using an algorithm. The input is user data, and the output is personalized activity suggestions based on the analysis results. In this process, Python scripts and cloud services are used to filter the data.

[0532] Step 3:

[0533] The server sends suggestions to the terminal based on the analysis results. The user receives recommended products and action suggestions via push notifications. The input is the analysis results, and the output is the suggestion message sent via push notification.

[0534] Step 4:

[0535] The user reviews the suggested actions on their device and chooses to take them. For example, when exchanging a recommended eco-friendly product, the user interface enables the action. The input is the recommendation message, and the output is the user's selection information.

[0536] Step 5:

[0537] The terminal monitors the user's actions and reports purchase information to the server in conjunction with the POS system. Specifically, it confirms the purchase item information and the completion of the event. The input is user actions, and the output is report data on the status of the actions.

[0538] Step 6:

[0539] The server evaluates the performance data, calculates points, and awards them to the user. The points are stored in a database such as Firebase. The input is performance report data, and the output is point information.

[0540] Step 7:

[0541] Finally, the server generates visual feedback and displays it to the user via the terminal. The user can then see the environmental contribution of their actions. The input is point information, and the output is visual feedback. During this process, the database and GUI are updated.

[0542] 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.

[0543] This invention is a system designed to support users in making sustainable behavioral changes, and it incorporates an emotion engine. This system collects user behavioral data and analyzes it using a generation algorithm to propose personalized environmental protection behaviors. The emotion engine then recognizes the user's emotions, enabling adjustments to the content and timing of suggestions, optimization of the reward system, and optimization of feedback.

[0544] The device collects data about the user's daily activities via smartphones and wearable devices. It also uses sensor devices such as cameras and microphones to collect data that infers emotions from the user's facial expressions and voice tone. This data is transmitted to a server.

[0545] The server analyzes the collected behavioral and emotional data using generation algorithms. Behavioral data is used to extract the user's daily behavioral patterns and interests, and to identify appropriate environmental protection actions. Emotional data, on the other hand, is used to determine the user's emotional state, and the content and timing of suggestions are adjusted based on this.

[0546] Based on the analysis results, the server generates a list of environmentally friendly actions best suited to the user. The suggestions are modified according to the user's emotional state, as recognized by the emotion engine, creating an environment where the user is more likely to take action. For example, if the user is tired, easily achievable actions are suggested; if they are feeling more positive, more challenging actions are suggested.

[0547] The device notifies the user of generated suggestions. As the user performs a selected suggestion, the device continuously tracks their emotional state and reports the situation to the server. Based on the degree of action completion and emotions, the server adjusts the reward system and optimizes points and rewards for the user. For example, actions that the user finds pleasing are given more points to increase motivation.

[0548] Furthermore, the server generates feedback based on emotional data and provides it visually through the terminal. For example, when a user takes an appropriate action, it provides a positive message that takes their emotional state into account, supporting continued engagement. This system makes it possible to guide users to optimal environmental protection actions tailored to their emotional state and behavioral characteristics, thereby promoting sustainable behavioral change.

[0549] The following describes the processing flow.

[0550] Step 1:

[0551] The device collects data on the user's daily activities through smartphones and wearable devices. This includes data on modes of transportation, activity time, and consumption behavior. The device also uses cameras and microphones to capture emotional data in real time, such as the user's facial expressions and voice tone.

[0552] Step 2:

[0553] The device sends collected behavioral and emotional data to the server. The data is encrypted during transmission and stored in an analysis database.

[0554] Step 3:

[0555] The server uses a generation algorithm to analyze behavioral patterns and user interests based on the received data. In particular, it identifies patterns such as consistent user interests and daily activities.

[0556] Step 4:

[0557] The server uses an emotion engine to analyze emotional data and determine the user's current emotional state. This includes emotional elements such as happiness, stress levels, and fatigue.

[0558] Step 5:

[0559] The server integrates the analysis results and proposes personalized environmental protection actions tailored to the user's emotional state. For example, if the server determines that the user is tired, it will suggest minor eco-friendly activities.

[0560] Step 6:

[0561] The device notifies the user of a list of generated environmental protection actions. The user reviews the suggested actions through the device and selects which actions to take.

[0562] Step 7:

[0563] The user performs the selected action. The device continues to monitor the user's emotional state to see if it affects the ongoing activity.

[0564] Step 8:

[0565] The device tracks the user's actions and reports progress data to the server. The degree of action completion and real-time emotional state are uploaded to the server.

[0566] Step 9:

[0567] The server operates a reward system based on the degree of activity completion and emotional state, awarding appropriate points to the user. For example, if a user completes an activity while experiencing positive emotions, they will receive additional points.

[0568] Step 10:

[0569] The server generates feedback that takes emotional data into account and provides it to the user through the terminal. The feedback highlights the environmental impact of the achieved actions and the positive emotional changes, encouraging the user to promote sustainable behavior.

[0570] (Example 2)

[0571] 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."

[0572] In modern society, it is important to sustainably promote individual environmental protection activities, but there is a lack of mechanisms to propose specific actions tailored to each individual and to support their implementation. Traditional methods have the challenge of not being able to provide appropriate suggestions and feedback that take into account the user's emotions and behavioral characteristics, and thus failing to maintain motivation.

[0573] 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.

[0574] In this invention, the server includes means for acquiring user behavior-related information, means for analyzing user behavior patterns and interests using computational algorithms, and means for analyzing user emotional data and dynamically adjusting the content and timing of suggestions. This makes it possible to individually propose the most suitable environmental protection activities for each user, thereby promoting sustainable behavioral change in users.

[0575] "User behavior-related information" refers to data about the user's daily activities, including location information, step count, and applications used.

[0576] A "computational algorithm" refers to a computational method used to analyze digital data and extract specific patterns or points of interest.

[0577] "Emotional data" refers to data acquired to understand a user's emotional state, and includes physiological indicators such as facial expressions, tone of voice, and heart rate.

[0578] "Means of dynamic adjustment" refers to a function that changes the content and timing of suggestions in real time in response to changes in the user's emotions and behavior.

[0579] "Environmental protection activities" refer to specific actions aimed at protecting or improving the natural environment, and include activities such as recycling, tree planting, and energy conservation.

[0580] This invention is a system that supports sustainable behavioral change in users and incorporates an emotion processing engine. The system uses technology to collect and analyze data on user behavior and emotions using multiple hardware and software components.

[0581] Hardware configuration:

[0582] The device acquires user behavior-related information via smartphones and wearable devices. Specifically, it utilizes data from the smartphone's built-in location sensor, pedometer, and applications being used. It also uses sensors such as cameras and microphones to acquire emotional data from the user's facial expressions and voice.

[0583] Software configuration:

[0584] The server processes the received data and uses a generative AI model to analyze the user's behavioral patterns and emotional state. This model generates suggestions based on behavioral and emotional data and dynamically adjusts their content. The server then recommends the most appropriate environmental protection activities for the user.

[0585] Specific example:

[0586] For example, if data shows a user has previously shown interest in recycling activities, the server will send a specific suggestion to the device, such as, "Why not participate in a local recycling event this weekend?" The device will then present this suggestion to the user as a notification, assisting them in making an action decision.

[0587] Prompt example:

[0588] "User behavioral data: Recycling participation history; emotional data: Detect positive emotional states. Based on this, generate the next environmental protection action to be taken."

[0589] This system makes it possible to encourage optimal and sustainable behavioral change tailored to the user's emotional state and behavioral characteristics.

[0590] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0591] Step 1:

[0592] The device collects data about the user's daily activities through smartphones and wearable devices. Specifically, it acquires location information, step count, and application usage history, and infers emotions from facial expressions and voice using the camera and microphone. This data is transmitted from the device to a server. The input consists of user data and emotion data, and the output is the data transmitted to the server.

[0593] Step 2:

[0594] The server uses a generative AI model to analyze the received behavioral and emotional data. It extracts user behavior patterns and interests from the behavioral data and evaluates the user's current emotional state from the emotional data. The input is data transmitted from the terminal, and the output is the basis for behavioral suggestions resulting from the analysis.

[0595] Step 3:

[0596] Based on the analysis results, the server generates the most suitable environmental protection activities for the user. The generating AI model outputs specific suggestions tailored to the user's characteristics and emotional state. For example, it might recommend participation in recycling activities. In this step, the analysis results are the input, and the output is the suggested content.

[0597] Step 4:

[0598] The device notifies the user of suggestions generated by the server. The user then decides which activity to perform based on these suggestions. Notifications are sent via smartphone push notifications or in-app messages. The input is the suggestion content, and the output is the notification sent to the user.

[0599] Step 5:

[0600] The user performs a selected activity, and throughout this process, the device continuously monitors the user's behavior and emotional state. The device reports this data to the server, recording the details of the activity's execution. The input is the user's activity data, and the output is the data reported to the server.

[0601] Step 6:

[0602] The server evaluates user behavior based on reported data and optimizes the reward system. This includes generating incentives and feedback that take into account achievement and emotional state. For example, a message such as "Your actions are a great contribution" might be generated. The input is activity evaluation data, and the output is the reward and feedback content.

[0603] (Application Example 2)

[0604] 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."

[0605] In modern society, while there is growing concern about environmental protection, there is a problem of a lack of concrete and effective support for individuals to practice sustainable behavior. Furthermore, because there are not enough means to encourage eco-friendly choices when shopping in physical stores, consumers do not perceive its importance, and sustainable behavior does not take root.

[0606] 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.

[0607] In this invention, the server includes means for collecting user behavior data, means for analyzing user behavior patterns and interests using a generation algorithm, and means for recognizing the user's emotional state and adaptively adjusting the content and timing of suggestions. This makes it possible to propose personalized eco-friendly behaviors in physical stores to users, increasing ease of implementation and satisfaction.

[0608] "Behavioral data" refers to information about users' daily activities and is useful for suggesting environmentally friendly behaviors.

[0609] A "generative algorithm" is a mathematical method used to analyze user behavior patterns and interests.

[0610] "Emotional state" refers to the psychological state inferred from the user's facial expressions and tone of voice, and is information used to adjust suggestions.

[0611] A "reward system" is a mechanism that awards points or rewards to users based on their actions, and is used to encourage sustainable behavior.

[0612] "Feedback" is information that visually communicates the results of suggestions or actions to the user, and is intended to motivate them to take further action.

[0613] A "physical store" is a facility that provides goods and services in a physical space, where customers visit and make purchases in person.

[0614] "Eco-friendly behavior" refers to all environmentally friendly choices and activities that contribute to the realization of a sustainable society.

[0615] This invention is a system that helps users easily practice eco-friendly behavior in physical stores. The system includes the following configuration:

[0616] First, the device collects data on the user's daily activities. Specifically, it uses smartphones and wearable devices to record the user's movements and behavioral patterns when visiting physical stores. This data is transmitted to the server in real time.

[0617] Next, the server uses a generative AI model to analyze the collected behavioral data. Using mathematical methods suited to this purpose, it extracts user behavior patterns and interests and proposes personalized, eco-friendly actions. It also collects emotional data using sensor devices such as cameras and microphones to recognize the user's emotional state in real time.

[0618] Based on this emotional state, the server dynamically adjusts the content and timing of the suggested actions. For example, it suggests proactive actions when the user is relaxed and simple actions when the user is stressed.

[0619] Furthermore, if a user takes an eco-friendly action suggested in a physical store, the terminal tracks the action and reports it to the server. Based on this information, the server awards points to the user according to a reward system and provides visual feedback through the terminal.

[0620] As a concrete example, when a user is shopping at a physical store, a smartphone app notifies them that "you can earn points by using an eco-bag." When the user uses the eco-bag, the server confirms the action and automatically awards the points.

[0621] An example of a prompt for a generative AI model is: "If the user's emotion is 'relaxed,' how should we suggest positive, eco-friendly actions?"

[0622] This allows users to easily incorporate sustainable behaviors into their habits without any burden.

[0623] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0624] Step 1:

[0625] The device collects the user's daily activity data in real time through smartphones and wearable devices. This data includes location information, distance traveled, and interaction history. Based on this information, the device extracts the dataset necessary for suggesting environmentally friendly behaviors and sends it to the server.

[0626] Step 2:

[0627] The server begins analyzing the received behavioral data using a generative AI model. Through algorithms, the server performs pattern recognition to identify user behavior patterns and interests. In this analysis, the generative AI model re-evaluates each data point and creates a list of individually optimized eco-friendly behaviors.

[0628] Step 3:

[0629] The device's sensor devices (camera, microphone, etc.) record the user's facial expressions and voice, and send this data to a server. The server analyzes this emotion data to infer the user's emotional state. This emotion analysis uses emotion recognition algorithms that evaluate changes in voice tone and facial expressions.

[0630] Step 4:

[0631] The server dynamically adjusts the content and timing of suggested actions based on the user's emotional state. At this stage, it prioritizes the list of actions according to the emotional state and selects the appropriate one. In this process, it may use a prompt message to the generating AI model such as, "If the user's emotion is 'relaxed,' what positive eco-friendly actions should be suggested?"

[0632] Step 5:

[0633] The suggested eco-friendly actions are notified to the user via the device. The user receives the notification through the application and is encouraged to act in accordance with the suggestion. These actions include using reusable shopping bags and purchasing certain environmentally friendly products.

[0634] Step 6:

[0635] If the user performs the suggested action, the device continues to track its execution and reports the execution data to the server. This report includes details about the type of action and its execution status, which the server uses to confirm the success of the action.

[0636] Step 7:

[0637] After confirming the execution, the server analyzes the user's actions and calculates reward points. It then awards the points to the user and provides visual feedback on the device. This feedback clearly indicates an evaluation of the actions and provides guidance for future actions.

[0638] 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.

[0639] 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.

[0640] 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.

[0641] [Fourth Embodiment]

[0642] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0643] 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.

[0644] 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).

[0645] 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.

[0646] 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.

[0647] 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).

[0648] 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.

[0649] 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.

[0650] 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.

[0651] 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.

[0652] 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.

[0653] 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.

[0654] 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".

[0655] This invention is a system for promoting user behavioral change and advancing sustainable environmental protection activities. This system collects user behavioral data and analyzes it using a generation algorithm to present personalized environmental protection behaviors. Furthermore, it promotes sustained behavioral change by providing users with points through a reward system and visual feedback.

[0656] In implementing the system, terminals collect daily behavioral data through smartphones and wearable devices with the user's permission. This data includes transportation methods, consumption activities, and location information. This data is sent to a server, which uses a generation algorithm to analyze the user's behavioral patterns and interests. Based on the analysis results, the server generates a list of environmentally friendly behaviors optimized for the user.

[0657] The presented list of actions is displayed on the device, allowing users to select actions that suit their interests and lifestyle. The status of selected actions is tracked by the device and reported to the server. The server operates a reward system based on this information, calculating and awarding points to users as they complete their actions. These points are visually displayed to the user on the device, serving as feedback to help them concretely understand their level of achievement and contribution to the environment.

[0658] For example, if a user selects the action "participate in a local cleanup activity on the weekend," the device tracks the achievement of the action through the user's location information and event participation confirmation. Based on this information, the server awards the user appropriate points and displays their contribution to eco-friendly activities. In this way, the system can provide users with specific and personalized feedback, naturally promoting sustainable behavior.

[0659] The following describes the processing flow.

[0660] Step 1:

[0661] The device collects data on the user's daily activities. It obtains data such as steps taken, location information, mode of transportation, and energy consumption from smartphones and wearable devices. This data is collected only with the user's permission.

[0662] Step 2:

[0663] The terminal sends the collected data to the server. It uses a communication protocol to securely transmit the data, allowing the server to prepare for data analysis.

[0664] Step 3:

[0665] The server analyzes the received data using a generation algorithm. It extracts user behavior patterns and trends, and identifies recommended environmental protection actions by referring to past data and the behavior history of similar users.

[0666] Step 4:

[0667] Based on the analysis results, the server generates a personalized list of environmental protection actions suitable for the user. This list includes the difficulty level of achievement and the expected CO2 reduction effect.

[0668] Step 5:

[0669] The device notifies the user of a generated list of environmental protection actions. The user can review the suggested actions through the device and select actions that suit their interests and lifestyle.

[0670] Step 6:

[0671] The user performs the suggested actions. Based on the selected actions, they begin taking specific actions in their daily life.

[0672] Step 7:

[0673] The device monitors the user's actions and reports progress to the server. It uses location information and sensor data to confirm the completion of actions and sends relevant data to the server.

[0674] Step 8:

[0675] The server calculates and awards points based on the user's performance using a reward system. Points are calculated as rewards according to the type of action and the degree of achievement.

[0676] Step 9:

[0677] The server generates feedback on the user's actions and sends it to the device along with data visualizing the environmental impact. The device then displays this information to the user, visually communicating the concrete results of their eco-friendly activities.

[0678] Step 10:

[0679] The device supports sustained efforts by presenting newly suggested actions and rewards to users who have received feedback, encouraging them to take on further challenges.

[0680] (Example 1)

[0681] 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".

[0682] Existing systems designed to promote environmental protection activities face the challenge of not effectively providing sustained motivation and feedback on user behavior. Furthermore, they fail to adequately address users' interests and lifestyles because they offer only general recommendations and cannot suggest specific actions optimized for individual users.

[0683] 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.

[0684] In this invention, the server includes means for collecting information about user behavior, means for analyzing user behavioral characteristics and interests using a generative AI model, and means for providing guidance to the user as visual information. This enables sustained motivation through personalized recommendations and rewards for environmentally friendly behaviors for the user.

[0685] "Means of collecting information about user behavior" refers to devices and methods for collecting data on users' daily activities, and includes technologies that utilize smartphones, wearable devices, and other similar tools.

[0686] A "generative AI model" is an artificial intelligence system that uses machine learning algorithms to learn specific patterns and trends from data and generate suggestions based on the user's behavioral characteristics and interests.

[0687] "Means for analyzing user behavioral characteristics and interests" refers to technologies that analyze information about a user's interests and lifestyle based on collected user data, and reveal their behavioral patterns.

[0688] A "means for recommending customized environmental protection actions" is a mechanism for suggesting personalized and specific environmental protection actions to users based on analyzed user data.

[0689] "Means of calculating rewards based on a redemption system" refers to a system that evaluates the user's level of achievement and calculates appropriate points or rewards accordingly.

[0690] "Means of providing guidance to users as visual information" refers to display methods and devices that allow users to visually confirm their level of achievement and progress, and typically refers to technologies that provide feedback through displays or similar means.

[0691] This invention relates to a system that promotes users' environmental protection activities. This system can recommend optimal actions for individual users by collecting user behavior data and analyzing it using a generative AI model. Furthermore, it encourages sustained behavioral change by awarding users points through a reward system and providing visual feedback.

[0692] The device collects behavioral information through smartphones and wearable devices with the user's permission. This information includes location data, means of transportation, and consumption activity logs. The collected data is encrypted end-to-end and transmitted to the server over the network.

[0693] The server processes the received data using a generating AI model to analyze the user's behavior patterns and interests. This AI model is based on machine learning algorithms and specifically performs big data processing to evaluate the user's past behavior and interests. An example of a prompt might be, "Based on this data, suggest environmentally friendly actions suitable for the user."

[0694] Based on the analysis results, the server generates a user-specific list of environmental protection actions. This list is sent to the device and displayed to the user. The user selects and performs actions that match their interests from this list. The device tracks the user's selected actions and reports the achievement status to the server based on location information and behavioral data.

[0695] The server operates a reward system based on the reported information, calculating and awarding points according to the user's actions. The terminal then visually displays these evaluation results to the user, allowing the user to check their contribution in real time.

[0696] This system enables information sharing and communication among users and also functions as a foundation for encouraging mutually sustainable behavior.

[0697] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0698] Step 1:

[0699] The device records the user's daily activities. This process is carried out using smartphones and wearable devices, and specifically includes GPS data, movement data from accelerometers, and application usage history. This data is provided to the system as input information indicating the user's current location and usage patterns.

[0700] Step 2:

[0701] The device sends the collected behavioral data to the server. This data transmission is end-to-end encrypted using a communication protocol. The input is the user's behavioral data, and the output is a data packet arranged for the server to receive.

[0702] Step 3:

[0703] The server begins analyzing the received data. The data is input into the generating AI model, and the prompt "Based on this data, suggest environmentally friendly actions suitable for the user" is used. The AI ​​model analyzes behavioral patterns using the input dataset and suggests specific environmentally friendly actions. Through this analysis process, recommended actions based on the user's interests and behavioral trends are obtained as output.

[0704] Step 4:

[0705] The server sends a list of recommended actions to the terminal. The input is the recommended actions from the generating AI, and the output is to send this information to the terminal in the appropriate format.

[0706] Step 5:

[0707] The terminal presents the user with recommended actions received from the server. The user can select an action that suits their interests from the displayed list. Here, the user's selection becomes the input, and the selection result becomes the output for the next process.

[0708] Step 6:

[0709] The user then performs the selected action. For example, this could be participating in a local cleanup activity on the weekend. As a result, information about the performed action is stored as input on the device.

[0710] Step 7:

[0711] The device tracks the user's actions and reports details of those actions to the server. The input is a log of the actions performed, and the output is the collected data sent to the server.

[0712] Step 8:

[0713] The server calculates reward points based on the user's performance. The input is user performance data, and the server outputs a specific number of points through data calculations.

[0714] Step 9:

[0715] The server sends the calculated points to the terminal, which then presents this information to the user as visual data. The user can then check their contribution and achievements to inform their next actions. The input is point information from the server, and the output is feedback showing the evaluation to the user.

[0716] (Application Example 1)

[0717] 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".

[0718] In modern society, promoting sustainable environmental conservation activities is a crucial issue, but it is not easy for individual consumers to concretely understand the environmental impact of their own actions and change their behavior. Furthermore, conventional methods lack sufficient promotion of environmental activities directly linked to purchasing behavior, and mechanisms to provide clear incentives to consumers are inadequate. Therefore, there is a need to provide a system that enables consumers to make environmentally friendly choices voluntarily and sustainably.

[0719] 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.

[0720] In this invention, the server includes means for collecting user behavior information, means for analyzing user behavioral trends and interests using a generation algorithm, and means for presenting personalized environmental conservation actions based on the analysis results. This makes it possible to analyze a user's purchasing behavior at physical stores and present personalized eco-recommended products. Furthermore, by calculating points after the purchase of a suggested product and displaying visual feedback, a clear incentive can be provided to the user, promoting sustainable behavior.

[0721] "User behavior information" refers to data related to the actions users take in their daily lives, such as consumption activities and travel patterns.

[0722] A "generative algorithm" refers to a process that analyzes user behavior and interests based on collected data to provide personalized recommendations.

[0723] "Environmental conservation actions" refer to actions taken by individuals with the aim of protecting the environment or using it sustainably, and include purchasing eco-friendly products and participating in recycling activities.

[0724] "Reward evaluation" refers to an evaluation criterion for providing incentives for actions taken by users, and involves calculating points based on the degree of action completion.

[0725] "Visually presenting" refers to a method of displaying information graphically through a user interface, enabling users to understand it intuitively.

[0726] A "sales facility" refers to a place where consumers purchase goods or services, and includes physical stores and online stores.

[0727] "Eco-recommended products" are products that have environmentally friendly characteristics and are individually recommended based on user behavior data.

[0728] "Communication methods" refer to methods that enable information exchange between users, and include messaging functions using digital devices and networks.

[0729] This invention is a system for promoting sustainable environmental conservation activities. By collecting and analyzing user behavior information, it proposes personalized eco-activities and provides incentives through reward evaluations.

[0730] In this system, terminals collect user behavior information and transmit it to a server. Specifically, smartphones and wearable devices are used to acquire data such as modes of transportation, consumption behavior, and location information. This data is aggregated on the server and analyzed using a generative AI model. The algorithms used for analysis are implemented in programming languages ​​such as Python, and cloud computing services such as AWS Lambda are utilized.

[0731] Based on the analysis results, the server suggests personalized environmental conservation actions and eco-friendly products to the user. This suggested information is sent to the user's device via push notifications, allowing the user to choose actions based on these suggestions.

[0732] When a user performs a recommended action, such as using an eco-bag to purchase a specific item at a store, the terminal communicates with the store's POS system to report the purchase information to the server. The server then operates a rewards system and awards points to the user. These points are stored in a database such as Firebase and displayed as visual feedback on the user's screen.

[0733] For example, if a user purchases locally produced goods at a food market and uses a coupon, the system will provide feedback stating, "Your local contribution has increased by 20 points." This allows users to instantly understand how their actions contribute to the environment.

[0734] An example of a prompt message is, "Think back to a day when you took an environmentally friendly action and freely express how you felt about it. Consider the impact that action had on you and others." This prompt is expected to encourage users to reflect on their environmental impact and increase their motivation for behavioral change.

[0735] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0736] Step 1:

[0737] The device collects user behavior information. Specifically, it uses smartphones and wearable devices to acquire information on mode of transport, consumption behavior, and location. This data is detected by sensors and applications and transmitted to a server via the internet. The input is the motion data acquired by the sensors, and the output is the data reported to the server.

[0738] Step 2:

[0739] The server analyzes the behavioral information it receives. Using a generative AI model, the server analyzes the user's behavioral tendencies and interests, and generates personalized action suggestions using an algorithm. The input is user data, and the output is personalized activity suggestions based on the analysis results. In this process, Python scripts and cloud services are used to filter the data.

[0740] Step 3:

[0741] The server sends suggestions to the terminal based on the analysis results. The user receives recommended products and action suggestions via push notifications. The input is the analysis results, and the output is the suggestion message sent via push notification.

[0742] Step 4:

[0743] The user reviews the suggested actions on their device and chooses to take them. For example, when exchanging a recommended eco-friendly product, the user interface enables the action. The input is the recommendation message, and the output is the user's selection information.

[0744] Step 5:

[0745] The terminal monitors the user's actions and reports purchase information to the server in conjunction with the POS system. Specifically, it confirms the purchase item information and the completion of the event. The input is user actions, and the output is report data on the status of the actions.

[0746] Step 6:

[0747] The server evaluates the performance data, calculates points, and awards them to the user. The points are stored in a database such as Firebase. The input is performance report data, and the output is point information.

[0748] Step 7:

[0749] Finally, the server generates visual feedback and displays it to the user via the terminal. The user can then see the environmental contribution of their actions. The input is point information, and the output is visual feedback. During this process, the database and GUI are updated.

[0750] 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.

[0751] This invention is a system designed to support users in making sustainable behavioral changes, and it incorporates an emotion engine. This system collects user behavioral data and analyzes it using a generation algorithm to propose personalized environmental protection behaviors. The emotion engine then recognizes the user's emotions, enabling adjustments to the content and timing of suggestions, optimization of the reward system, and optimization of feedback.

[0752] The device collects data about the user's daily activities via smartphones and wearable devices. It also uses sensor devices such as cameras and microphones to collect data that infers emotions from the user's facial expressions and voice tone. This data is transmitted to a server.

[0753] The server analyzes the collected behavioral and emotional data using generation algorithms. Behavioral data is used to extract the user's daily behavioral patterns and interests, and to identify appropriate environmental protection actions. Emotional data, on the other hand, is used to determine the user's emotional state, and the content and timing of suggestions are adjusted based on this.

[0754] Based on the analysis results, the server generates a list of environmentally friendly actions best suited to the user. The suggestions are modified according to the user's emotional state, as recognized by the emotion engine, creating an environment where the user is more likely to take action. For example, if the user is tired, easily achievable actions are suggested; if they are feeling more positive, more challenging actions are suggested.

[0755] The device notifies the user of generated suggestions. As the user performs a selected suggestion, the device continuously tracks their emotional state and reports the situation to the server. Based on the degree of action completion and emotions, the server adjusts the reward system and optimizes points and rewards for the user. For example, actions that the user finds pleasing are given more points to increase motivation.

[0756] Furthermore, the server generates feedback based on emotional data and provides it visually through the terminal. For example, when a user takes an appropriate action, it provides a positive message that takes their emotional state into account, supporting continued engagement. This system makes it possible to guide users to optimal environmental protection actions tailored to their emotional state and behavioral characteristics, thereby promoting sustainable behavioral change.

[0757] The following describes the processing flow.

[0758] Step 1:

[0759] The device collects data on the user's daily activities through smartphones and wearable devices. This includes data on modes of transportation, activity time, and consumption behavior. The device also uses cameras and microphones to capture emotional data in real time, such as the user's facial expressions and voice tone.

[0760] Step 2:

[0761] The device sends collected behavioral and emotional data to the server. The data is encrypted during transmission and stored in an analysis database.

[0762] Step 3:

[0763] The server uses a generation algorithm to analyze behavioral patterns and user interests based on the received data. In particular, it identifies patterns such as consistent user interests and daily activities.

[0764] Step 4:

[0765] The server uses an emotion engine to analyze emotional data and determine the user's current emotional state. This includes emotional elements such as happiness, stress levels, and fatigue.

[0766] Step 5:

[0767] The server integrates the analysis results and proposes personalized environmental protection actions tailored to the user's emotional state. For example, if the server determines that the user is tired, it will suggest minor eco-friendly activities.

[0768] Step 6:

[0769] The device notifies the user of a list of generated environmental protection actions. The user reviews the suggested actions through the device and selects which actions to take.

[0770] Step 7:

[0771] The user performs the selected action. The device continues to monitor the user's emotional state to see if it affects the ongoing activity.

[0772] Step 8:

[0773] The device tracks the user's actions and reports progress data to the server. The degree of action completion and real-time emotional state are uploaded to the server.

[0774] Step 9:

[0775] The server operates a reward system based on the degree of activity completion and emotional state, awarding appropriate points to the user. For example, if a user completes an activity while experiencing positive emotions, they will receive additional points.

[0776] Step 10:

[0777] The server generates feedback that takes emotional data into account and provides it to the user through the terminal. The feedback highlights the environmental impact of the achieved actions and the positive emotional changes, encouraging the user to promote sustainable behavior.

[0778] (Example 2)

[0779] 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".

[0780] In modern society, it is important to sustainably promote individual environmental protection activities, but there is a lack of mechanisms to propose specific actions tailored to each individual and to support their implementation. Traditional methods have the challenge of not being able to provide appropriate suggestions and feedback that take into account the user's emotions and behavioral characteristics, and thus failing to maintain motivation.

[0781] 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.

[0782] In this invention, the server includes means for acquiring user behavior-related information, means for analyzing user behavior patterns and interests using computational algorithms, and means for analyzing user emotional data and dynamically adjusting the content and timing of suggestions. This makes it possible to individually propose the most suitable environmental protection activities for each user, thereby promoting sustainable behavioral change in users.

[0783] "User behavior-related information" refers to data about the user's daily activities, including location information, step count, and applications used.

[0784] A "computational algorithm" refers to a computational method used to analyze digital data and extract specific patterns or points of interest.

[0785] "Emotional data" refers to data acquired to understand a user's emotional state, and includes physiological indicators such as facial expressions, tone of voice, and heart rate.

[0786] "Means of dynamic adjustment" refers to a function that changes the content and timing of suggestions in real time in response to changes in the user's emotions and behavior.

[0787] "Environmental protection activities" refer to specific actions aimed at protecting or improving the natural environment, and include activities such as recycling, tree planting, and energy conservation.

[0788] This invention is a system that supports sustainable behavioral change in users and incorporates an emotion processing engine. The system uses technology to collect and analyze data on user behavior and emotions using multiple hardware and software components.

[0789] Hardware configuration:

[0790] The device acquires user behavior-related information via smartphones and wearable devices. Specifically, it utilizes data from the smartphone's built-in location sensor, pedometer, and applications being used. It also uses sensors such as cameras and microphones to acquire emotional data from the user's facial expressions and voice.

[0791] Software configuration:

[0792] The server processes the received data and uses a generative AI model to analyze the user's behavioral patterns and emotional state. This model generates suggestions based on behavioral and emotional data and dynamically adjusts their content. The server then recommends the most appropriate environmental protection activities for the user.

[0793] Specific example:

[0794] For example, if data shows a user has previously shown interest in recycling activities, the server will send a specific suggestion to the device, such as, "Why not participate in a local recycling event this weekend?" The device will then present this suggestion to the user as a notification, assisting them in making an action decision.

[0795] Prompt example:

[0796] "User behavioral data: Recycling participation history; emotional data: Detect positive emotional states. Based on this, generate the next environmental protection action to be taken."

[0797] This system makes it possible to encourage optimal and sustainable behavioral change tailored to the user's emotional state and behavioral characteristics.

[0798] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0799] Step 1:

[0800] The device collects data about the user's daily activities through smartphones and wearable devices. Specifically, it acquires location information, step count, and application usage history, and infers emotions from facial expressions and voice using the camera and microphone. This data is transmitted from the device to a server. The input consists of user data and emotion data, and the output is the data transmitted to the server.

[0801] Step 2:

[0802] The server uses a generative AI model to analyze the received behavioral and emotional data. It extracts user behavior patterns and interests from the behavioral data and evaluates the user's current emotional state from the emotional data. The input is data transmitted from the terminal, and the output is the basis for behavioral suggestions resulting from the analysis.

[0803] Step 3:

[0804] Based on the analysis results, the server generates the most suitable environmental protection activities for the user. The generating AI model outputs specific suggestions tailored to the user's characteristics and emotional state. For example, it might recommend participation in recycling activities. In this step, the analysis results are the input, and the output is the suggested content.

[0805] Step 4:

[0806] The device notifies the user of suggestions generated by the server. The user then decides which activity to perform based on these suggestions. Notifications are sent via smartphone push notifications or in-app messages. The input is the suggestion content, and the output is the notification sent to the user.

[0807] Step 5:

[0808] The user performs a selected activity, and throughout this process, the device continuously monitors the user's behavior and emotional state. The device reports this data to the server, recording the details of the activity's execution. The input is the user's activity data, and the output is the data reported to the server.

[0809] Step 6:

[0810] The server evaluates user behavior based on reported data and optimizes the reward system. This includes generating incentives and feedback that take into account achievement and emotional state. For example, a message such as "Your actions are a great contribution" might be generated. The input is activity evaluation data, and the output is the reward and feedback content.

[0811] (Application Example 2)

[0812] 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".

[0813] In modern society, while there is growing concern about environmental protection, there is a problem of a lack of concrete and effective support for individuals to practice sustainable behavior. Furthermore, because there are not enough means to encourage eco-friendly choices when shopping in physical stores, consumers do not perceive its importance, and sustainable behavior does not take root.

[0814] 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.

[0815] In this invention, the server includes means for collecting user behavior data, means for analyzing user behavior patterns and interests using a generation algorithm, and means for recognizing the user's emotional state and adaptively adjusting the content and timing of suggestions. This makes it possible to propose personalized eco-friendly behaviors in physical stores to users, increasing ease of implementation and satisfaction.

[0816] "Behavioral data" refers to information about users' daily activities and is useful for suggesting environmentally friendly behaviors.

[0817] A "generative algorithm" is a mathematical method used to analyze user behavior patterns and interests.

[0818] "Emotional state" refers to the psychological state inferred from the user's facial expressions and tone of voice, and is information used to adjust suggestions.

[0819] A "reward system" is a mechanism that awards points or rewards to users based on their actions, and is used to encourage sustainable behavior.

[0820] "Feedback" is information that visually communicates the results of suggestions or actions to the user, and is intended to motivate them to take further action.

[0821] A "physical store" is a facility that provides goods and services in a physical space, where customers visit and make purchases in person.

[0822] "Eco-friendly behavior" refers to all environmentally friendly choices and activities that contribute to the realization of a sustainable society.

[0823] This invention is a system that helps users easily practice eco-friendly behavior in physical stores. The system includes the following configuration:

[0824] First, the device collects data on the user's daily activities. Specifically, it uses smartphones and wearable devices to record the user's movements and behavioral patterns when visiting physical stores. This data is transmitted to the server in real time.

[0825] Next, the server uses a generative AI model to analyze the collected behavioral data. Using mathematical methods suited to this purpose, it extracts user behavior patterns and interests and proposes personalized, eco-friendly actions. It also collects emotional data using sensor devices such as cameras and microphones to recognize the user's emotional state in real time.

[0826] Based on this emotional state, the server dynamically adjusts the content and timing of the suggested actions. For example, it suggests proactive actions when the user is relaxed and simple actions when the user is stressed.

[0827] Furthermore, if a user takes an eco-friendly action suggested in a physical store, the terminal tracks the action and reports it to the server. Based on this information, the server awards points to the user according to a reward system and provides visual feedback through the terminal.

[0828] As a concrete example, when a user is shopping at a physical store, a smartphone app notifies them that "you can earn points by using an eco-bag." When the user uses the eco-bag, the server confirms the action and automatically awards the points.

[0829] An example of a prompt for a generative AI model is: "If the user's emotion is 'relaxed,' how should we suggest positive, eco-friendly actions?"

[0830] This allows users to easily incorporate sustainable behaviors into their habits without any burden.

[0831] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0832] Step 1:

[0833] The device collects the user's daily activity data in real time through smartphones and wearable devices. This data includes location information, distance traveled, and interaction history. Based on this information, the device extracts the dataset necessary for suggesting environmentally friendly behaviors and sends it to the server.

[0834] Step 2:

[0835] The server begins analyzing the received behavioral data using a generative AI model. Through algorithms, the server performs pattern recognition to identify user behavior patterns and interests. In this analysis, the generative AI model re-evaluates each data point and creates a list of individually optimized eco-friendly behaviors.

[0836] Step 3:

[0837] The device's sensor devices (camera, microphone, etc.) record the user's facial expressions and voice, and send this data to a server. The server analyzes this emotion data to infer the user's emotional state. This emotion analysis uses emotion recognition algorithms that evaluate changes in voice tone and facial expressions.

[0838] Step 4:

[0839] The server dynamically adjusts the content and timing of suggested actions based on the user's emotional state. At this stage, it prioritizes the list of actions according to the emotional state and selects the appropriate one. In this process, it may use a prompt message to the generating AI model such as, "If the user's emotion is 'relaxed,' what positive eco-friendly actions should be suggested?"

[0840] Step 5:

[0841] The suggested eco-friendly actions are notified to the user via the device. The user receives the notification through the application and is encouraged to act in accordance with the suggestion. These actions include using reusable shopping bags and purchasing certain environmentally friendly products.

[0842] Step 6:

[0843] If the user performs the suggested action, the device continues to track its execution and reports the execution data to the server. This report includes details about the type of action and its execution status, which the server uses to confirm the success of the action.

[0844] Step 7:

[0845] After confirming the execution, the server analyzes the user's actions and calculates reward points. It then awards the points to the user and provides visual feedback on the device. This feedback clearly indicates an evaluation of the actions and provides guidance for future actions.

[0846] 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.

[0847] 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.

[0848] 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.

[0849] 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.

[0850] 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.

[0851] 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.

[0852] 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.

[0853] 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.

[0854] 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."

[0855] 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.

[0856] 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.

[0857] 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.

[0858] 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.

[0859] 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.

[0860] 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.

[0861] 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.

[0862] 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.

[0863] 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.

[0864] 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.

[0865] 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.

[0866] 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 to be incorporated by reference.

[0867] The following is further disclosed regarding the embodiments described above.

[0868] (Claim 1)

[0869] Means for collecting user behavior data,

[0870] A means of analyzing user behavior patterns and interests using a generation algorithm,

[0871] Based on the analysis results, a means to propose individualized environmental protection actions,

[0872] A means of tracking user behavior and awarding points based on a reward system,

[0873] A means of providing users with visual feedback,

[0874] A device that includes this.

[0875] (Claim 2)

[0876] The apparatus according to claim 1, further comprising means for suggesting additional environmental protection actions to the user according to the degree of action achieved.

[0877] (Claim 3)

[0878] The apparatus according to claim 1, which includes means for information sharing and communication among users, and which promotes sustainable behavior among users.

[0879] "Example 1"

[0880] (Claim 1)

[0881] Means for collecting information about user behavior,

[0882] Means for transmitting data to a central processing unit via a network,

[0883] A means of analyzing user behavioral characteristics and interests using a generative AI model,

[0884] Based on the analysis results, a means of recommending customized environmental protection actions,

[0885] A means for monitoring the progress of user actions and calculating rewards based on a redemption system,

[0886] A means of providing guidance to the user as visual information,

[0887] A system that includes this.

[0888] (Claim 2)

[0889] The system according to claim 1, further comprising means for presenting the user with additional environmental protection actions depending on the status of the actions performed.

[0890] (Claim 3)

[0891] The system according to claim 1, which includes means for data exchange and communication among users, and which promotes sustained behavioral change among users.

[0892] "Application Example 1"

[0893] (Claim 1)

[0894] Means for collecting user behavior information,

[0895] A means for analyzing user behavioral trends and interests using a generation algorithm,

[0896] Based on the analysis results, a means of presenting individualized environmental conservation actions,

[0897] A means of observing the user's actions and allocating points based on reward evaluations,

[0898] A means of visually presenting results to the user,

[0899] A means of acquiring behavioral and consumption information at sales facilities and presenting personalized eco-friendly recommended products,

[0900] A means of calculating points after purchasing a suggested product and displaying visual feedback,

[0901] A system that includes this.

[0902] (Claim 2)

[0903] The system according to claim 1, further comprising means for presenting the user with additional environmental conservation actions according to the degree of action achieved.

[0904] (Claim 3)

[0905] The system according to claim 1, which includes means for information sharing and communication among users, and which promotes sustainable activities for users.

[0906] "Example 2 of combining an emotion engine"

[0907] (Claim 1)

[0908] Means for obtaining user behavior-related information,

[0909] A means for analyzing user behavior patterns and interests using computational algorithms,

[0910] Based on the analysis results, a means to recommend individualized environmental protection activities,

[0911] A means of analyzing user sentiment data and dynamically adjusting the content and timing of suggestions,

[0912] A means of monitoring user behavior and providing incentives based on a reward system,

[0913] A means of visually displaying feedback to the user,

[0914] A system that includes this.

[0915] (Claim 2)

[0916] The system according to claim 1, comprising means for recommending appropriate additional environmental protection activities to the user based on the degree of behavioral achievement and emotional state.

[0917] (Claim 3)

[0918] The system according to claim 1, which includes means for users to share information with each other and facilitate communication, thereby assisting users in taking sustainable actions.

[0919] "Application example 2 when combining with an emotional engine"

[0920] (Claim 1)

[0921] Means for collecting user behavior data,

[0922] A means of analyzing user behavior patterns and interests using a generation algorithm,

[0923] Based on the analysis results, a means to propose individualized environmental protection actions,

[0924] A means of recognizing the user's emotional state and adaptively adjusting the content and timing of suggestions,

[0925] A means of tracking user behavior and awarding points based on a reward system,

[0926] A means of providing users with visual feedback,

[0927] In physical stores, a means of making action suggestions to promote eco-friendly behavior,

[0928] A system that includes this.

[0929] (Claim 2)

[0930] The system according to claim 1, further comprising means for suggesting additional environmental protection actions to the user according to the degree of action achieved.

[0931] (Claim 3)

[0932] The system according to claim 1, comprising means for adjusting the reward system based on the user's emotional state and granting the user appropriate points and rewards. [Explanation of Symbols]

[0933] 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. Means for collecting user behavior data, A means of analyzing user behavior patterns and interests using a generation algorithm, Based on the analysis results, a means to propose individualized environmental protection actions, A means of tracking user behavior and awarding points based on a reward system, A means of providing users with visual feedback, A device that includes this.

2. The apparatus according to claim 1, further comprising means for suggesting additional environmental protection actions to the user according to the degree of action achieved.

3. The apparatus according to claim 1, which includes means for information sharing and communication among users and promotes sustainable user behavior.

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