System

Abstract

A system is provided.SOLUTION: A system comprising: an input unit that a user uses for convenience; a data receiving unit that receives input data; a data analyzing unit that analyzes the received data; a point calculating unit that calculates a point based on an analysis result; a point giving unit that gives the calculated point to the user; a notifying unit that notifies the given point; a linking unit that links data with another social networking service; and a storing unit that stores and manages accumulated data.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Although the importance of environmental protection is widely known, it is difficult for ordinary consumers to understand the extent to which their everyday behavioral changes contribute to environmental protection, making it difficult to maintain sustained motivation. Therefore, a system is needed that allows consumers to easily continue environmental protection activities and maintain sustained interest by sharing and evaluating their actions. [Means for solving the problem]

[0005] The present invention is a system that includes an input means used by users for convenience, a data receiving means for receiving input data, a data analysis means for analyzing the received data, a point calculation means for calculating points based on the analysis results, a point awarding means for awarding the calculated points to users, a notification means for notifying the users of the awarded points, a linking means for linking data to other social networking services, and a storage means for saving and managing the accumulated data. This system appropriately evaluates users' environmental protection activities, motivates them to continue their activities by awarding points, and is expected to promote usage by spreading the system through SNS integration.

[0006] An "input means" is a device or function that provides an interface for a user to input data such as text or images.

[0007] "Data receiving means" is a function that receives data sent through input means and stores and processes it.

[0008] "Data analysis means" is a function that analyzes received data using generative AI technology and image analysis technology, and evaluates the content and meaning of the data.

[0009] The "point calculation means" is a function for calculating points to be acquired by a user based on the analysis results obtained by the data analysis means.

[0010] The "points granting means" is a function for granting calculated points to a user's account.

[0011] "Notification means" is a function for notifying users of the results of point allocation and other important information.

[0012] "Linking means" refers to a function for linking and sharing information such as input data and awarded points with other social networking services.

[0013] "Storage means" refers to a function for long-term storage and management of input data, analysis results, awarded points, and other related information. [Brief explanation of the drawings]

[0014] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 11] FIG. 3 is a sequence diagram showing a processing flow of the data processing system according to the first embodiment. [Figure 12] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 1. [Figure 13] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system according to the second embodiment when an emotion engine is combined. [Figure 14]FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 2 when an emotion engine is combined. DETAILED DESCRIPTION OF THE INVENTION

[0015] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

[0016] First, the terms used in the following description will be explained.

[0017] In the following embodiments, a coded processor (hereinafter simply referred to as a "processor") may be a single arithmetic device or a combination of multiple arithmetic devices. Furthermore, a processor may be a single type of arithmetic device or a combination of multiple types of arithmetic devices. Examples of arithmetic devices include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), and an APU (Accelerated Processing Unit).

[0018] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

[0019] In the following embodiments, the coded storage is one or more non-volatile storage devices that store various programs, various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), and magnetic tapes.

[0020] In the following embodiments, a communication I / F (Interface) with a symbol is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), Bluetooth (registered trademark), etc.

[0021] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

[0022] [First embodiment]

[0023] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0024] 1, a data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0025] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0026] The smart device 14 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0027] The reception device 38 includes a touch panel 38A, a microphone 38B, and the like, and receives user input. The touch panel 38A detects contact with an indicator (for example, a pen or a finger) to receive user input by the touch of the indicator. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0028] 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 of expression that the user 20 can perceive (for example, audio and / or text). The display 40A displays visible information such as text and images in accordance with instructions from the processor 46. The speaker 40B outputs audio in accordance with instructions from the processor 46. The camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0029] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

[0030] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0031] 2, in the data processing device 12, a specific process is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0032] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0033] In the smart device 14, the processor 46 performs the reception output process. The storage 50 stores a reception output program 60. The reception output program 60 is used in conjunction with the 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 process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0034] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0035] This invention is a system that awards points to users by entering "good deeds for the Earth" into a social networking app. This system provides continuous motivation for users to continue environmental protection activities easily, share their actions, and receive rewards.

[0036] System configuration

[0037] 1. Input Method

[0038] Users use social media apps to input text and images. For example, they might write, "I commuted by bicycle today," and attach a photo of their bicycle. This input method is done on devices such as smartphones and tablets.

[0039] 2. Means of receiving data

[0040] The data transmitted from the terminal is received by the server, which receives the data via the Internet and stores it for analysis.

[0041] 3. Data Analysis Methods

[0042] The server analyzes the received text and images using AI generative technology and image analysis technology. For example, the text analysis module extracts the keyword "bicycle commuting," and the image analysis module verifies that the attached photo is of a bicycle.

[0043] 4. Point Calculation Method

[0044] The server calculates the contribution of an action based on the results of the data analysis means, for example, "commuting by bicycle" is evaluated as being worth 10 points to environmental protection.

[0045] 5. Points Awarding Method

[0046] The server will then calculate the points and credit them to the user's account, which the user can view within the app.

[0047] 6. Means of notification

[0048] The server notifies the terminal of the result of point allocation. For example, a message saying "10 points have been allocated" is displayed.

[0049] 7. Collaboration Methods

[0050] The server then shares the user's posts with other social networking services. For example, if the user has enabled it, it can automatically post to Twitter or Facebook, "I commuted by bicycle today."

[0051] 8. Preservation means

[0052] The server stores all receipt data, analysis results, and point allocation results. This data is used for system management and further analysis.

[0053] Specific examples

[0054] A user enters "I used my own bottle today" into a social media app and attaches a photo. When this information is entered using a smartphone, the data is sent to a server via the internet. The server analyzes the received data using generative AI and checks the keyword "I used my own bottle" and the content of the photo. If this action is evaluated as contributing 5 points to environmental protection, the server will add 5 points to the user's account and notify them of this.

[0055] At the same time, this post is automatically posted to other social media accounts (e.g., Twitter) selected by the user, so that their personal bottle use activities can be widely shared.Finally, the server stores all data and uses it for long-term analysis and management.

[0056] In this way, the system provides users with an incentive to take sustainable actions to protect the environment and a means to widely share the results.

[0057] The processing flow will be explained below.

[0058] Step 1:

[0059] A user launches a social media app and accesses the posting form.

[0060] Step 2:

[0061] The user enters the text "I commuted to work by bicycle today" into the posting form and attaches a photo of their bicycle.

[0062] Step 3:

[0063] The user presses the submit button to confirm the input.

[0064] Step 4:

[0065] The terminal packages the input text and image data and sends it to the server.

[0066] Step 5:

[0067] The server receives the data sent from the terminal.

[0068] Step 6:

[0069] The server passes the received data to a text analysis module, which analyzes the text.

[0070] Specific operation: The generative AI analyzes the text and extracts the keyword "bicycle commuting."

[0071] Step 7:

[0072] The server passes the image data to an image analysis module, which analyzes the image.

[0073] What it does: Uses image classification techniques to verify that the attached image is a photo of a bicycle.

[0074] Step 8:

[0075] The server scores the contribution of the behavior based on the results of text analysis and image analysis.

[0076] Specific action: The action of "commuting by bicycle" is given 10 points.

[0077] Step 9:

[0078] The server references the user's account database and adds the calculated 10 points to the user's account.

[0079] Step 10:

[0080] The server generates a message informing the user of the points being awarded.

[0081] Specific action: Create a notification that says "10 points awarded."

[0082] Step 11:

[0083] The server generates a notification message and sends it to the terminal.

[0084] Step 12:

[0085] Display notification messages received by the device to the user.

[0086] What happens: A notification will appear in the app saying "10 points awarded."

[0087] Step 13:

[0088] The server then links the posts to other social networking services based on the user's settings.

[0089] Specific operation: Converts the posted data into a specified format and sends it to Twitter or Facebook.

[0090] Step 14:

[0091] The server stores the receipt data, analysis results, and point allocation results in a database.

[0092] Step 15:

[0093] The server obtains advertising revenue data from the management system and secures the points funds.

[0094] Step 16:

[0095] The server manages a portion of the advertising revenue as a source of points redemption, ensuring the sustainability of the system.

[0096] Example 1

[0097] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0098] Currently, there are only a limited number of systems that promote environmental protection activities, and most of them have difficulty providing continuous motivation to users. Furthermore, there is a lack of systems that analyze the accuracy of input information, appropriately evaluate it, and provide rewards. Furthermore, these systems are rarely linked to other online platforms and are rarely widely recognized, which poses the challenge of lacking continuous motivation.

[0099] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0100] In this invention, the server includes an input means used by users for operation, a data receiving means for receiving input information, a data analysis means for analyzing the received information, a point calculation means for calculating points based on the analysis results, a point awarding means for awarding the calculated points to users, a notification means for notifying the users of the awarded points, a linking means for linking the information to other online platforms, and a storage means for saving and managing the accumulated information. This allows users to easily record their environmental protection activities, widely share those activities, and receive continuous motivation.

[0101] "User" refers to the entity that performs operations or inputs, and includes individuals and organizations.

[0102] "Input means" refers to a device or method for a user to input information such as text and images.

[0103] "Data receiving means" refers to a means for receiving input information and transmitting it to a server.

[0104] "Data Analysis Tools" refers to techniques and devices for analyzing received text and images.

[0105] "Point calculation means" refers to the technology or device used to evaluate user behavior and calculate points based on the results of data analysis.

[0106] "Points Granting Means" refers to the means by which calculated points are added to a User's account.

[0107] "Notification means" refers to the technology or device that notifies users of the results of point allocation.

[0108] "Linkage means" refers to the technology or device that allows users to share their input information and activities with other online platforms.

[0109] "Storage means" refers to the technology and devices used to store and manage received information, analysis results, and point allocation results.

[0110] "Artificial intelligence technology" refers to the technology of analyzing input text using techniques such as machine learning, deep learning, and natural language processing.

[0111] "Image analysis technology" refers to technology for analyzing input images and recognizing their contents.

[0112] "Online platform" refers to services and websites provided via the Internet, including social networking sites and information sharing services.

[0113] This invention is a system that awards points to users by entering "good deeds for the Earth" into a social networking app. This system provides continuous motivation for users to continue environmental protection activities easily, share their actions, and receive rewards.

[0114] First, users access a social networking app using a device such as a smartphone or tablet. The application provides an input method for users to enter their environmental conservation activities using text and images. For example, a user might enter, "I commuted to work by bicycle today," and attach a photo of their bicycle.

[0115] The data entered by the user through the input means is sent to the server via the data receiving means. The data sent from the terminal arrives at the server via the Internet and is temporarily stored in a database.

[0116] The server then uses data analysis tools to analyze the received text and images. Specifically, it combines generative AI technology (artificial intelligence technology) and image analysis technology to perform text analysis and image recognition. For example, the text analysis module extracts the keyword "bicycle commuting," and the image analysis module recognizes bicycles from the attached photo.

[0117] After the analysis is completed, the server uses a point calculation means to evaluate the contribution of the behavior based on the analysis results and calculate points. For example, "commuting by bicycle" is evaluated as being worth 10 points for environmental protection. This evaluation standard is preset in the system.

[0118] Once the points are calculated, the server activates a points granting means to grant the points to the user's account. The calculated points are reflected in the user's account information. The user can check the current number of points through the application.

[0119] The result of point allocation will be notified to the user through a notification method. Specifically, a message saying "10 points have been allocated" will be sent to the user's smartphone in real time.

[0120] Furthermore, through the integration method, users' posts about environmental protection activities can be automatically shared on other social networking sites (online platforms). For example, if the user has set it up in advance, the post can also be linked and shared on Twitter, Facebook, etc. This allows the user's activities to be widely recognized.

[0121] Finally, the server uses storage means to store the receipt data, analysis results, point allocation results, etc. for a long period of time. This data is used for system management and further analysis.

[0122] Specific examples

[0123] A user types "I used my own bottle today" into a social media app and attaches a photo. This data is sent from the device to a server via the internet. The server uses generative AI technology to analyze the received data and checks the keyword "I used my own bottle" and the content of the photo. This action is assessed as contributing 5 points to environmental protection, and 5 points are awarded to the user's account. The user is also notified of the result, and depending on their settings, it is automatically posted to other social media platforms such as Twitter. Finally, the server stores all data and uses it for long-term analysis and management.

[0124] Prompt Sentence Examples

[0125] "When a user enters their environmental protection actions into a social media app, the server analyzes the text and images using generative AI technology, calculates and awards points, and notifies the user of the results, and shares the post on other social media platforms. Please explain with a concrete example."

[0126] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0127] Step 1:

[0128] A user launches a social networking app and inputs text and images. For example, they might type, "I commuted by bicycle today," and attach a photo of their bicycle. Input is done using a device such as a smartphone or tablet.

[0129] Input: Text and images entered by the user.

[0130] Output: Data packets that the device sends to the server.

[0131] Specific behavior: A user enters text into an app's input field and attaches an image.

[0132] Step 2:

[0133] The terminal transmits the input data to a server via the Internet.

[0134] Input: Text and images entered by the user.

[0135] Output: The data sent to the server.

[0136] Specific operation: The terminal packetizes the data and sends an HTTP request. The data is encrypted and sent.

[0137] Step 3:

[0138] The server temporarily stores the data received from the terminal in a database.

[0139] Input: Data sent from the terminal.

[0140] Output: Data stored in the database.

[0141] Specific operation: The server parses the received JSON format data and issues an INSERT statement to the database.

[0142] Step 4:

[0143] The server analyzes the received text and images using generative AI technology and image analysis technology.

[0144] Input: Text and image data stored in a database.

[0145] Output: Parsed keywords and verification results.

[0146] How it works: The generative AI model analyzes the text and extracts the keyword "bicycle commuting." Image analysis technology recognizes bicycles from images.

[0147] Step 5:

[0148] The server calculates the contribution of the behavior based on the results of the data analysis, and calculates points.

[0149] Input: Parsed keywords and verification results.

[0150] Output: The calculated points.

[0151] What happens: The server calculates points using predefined rating logic. "Commuting by bicycle" is rated as 10 points.

[0152] Step 6:

[0153] The server credits the calculated points to the user's account.

[0154] Input: The calculated point.

[0155] Output: Points added to the user's account information.

[0156] Specific operation: The server executes an SQL query to update the user's account information and add new points to the current points.

[0157] Step 7:

[0158] The server notifies the terminal of the result of the point allocation.

[0159] Input: Point award results.

[0160] Output: Notification message to user terminal.

[0161] Specific operation: The server sends a push notification using a real-time notification service (e.g., Firebase Cloud Messaging).

[0162] Step 8:

[0163] The server then links the user's posts to other online platforms.

[0164] Input: User posts and collaboration settings.

[0165] Output: Posting to other online platforms.

[0166] Specific operation: The server uses OAuth to access the user's social media account and sends an API request to automatically post the configured content to Twitter or Facebook, for example.

[0167] Step 9:

[0168] The server stores and manages the receipt data, analysis results, and point allocation results over the long term.

[0169] Input: Received data, analysis results, point allocation results.

[0170] Output: Saved data.

[0171] Specific operation: The server issues an INSERT statement to the database to permanently store various data. The stored data can be used for future analysis and system audits.

[0172] Through these steps, users can record their environmental protection actions, receive points for their actions, and share them more widely, providing lasting motivation.

[0173] (Application example 1)

[0174] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0175] There is a need for a system that can sustainably encourage the efforts of individuals who engage in environmental protection activities and effectively share their actions. Conventional systems simply aggregate the environmental protection activities performed by users, without providing specific rewards or widespread sharing, making it difficult to maintain individual motivation. Furthermore, there is a lack of means to objectively evaluate the effectiveness of environmental protection activities. To solve these issues, a system is needed that automatically analyzes users' reports and provides motivation for continuous environmental protection activities through points awarding and sharing on social media.

[0176] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0177] In this invention, the server includes an input means used by users for convenience, a data receiving means for receiving input data, a data analysis means for analyzing the received data, a point calculation means for calculating points based on the analysis results, a point awarding means for awarding the calculated points to users, a notification means for notifying the users of the awarded points, a linking means for linking data to other social networking services, a storage means for saving and managing accumulated data, a generation AI technology means for using generation AI technology as data analysis means, an image analysis means for using image analysis technology as image analysis means, and an SNS sharing means for sharing posts on SNS based on the user's reports. This allows points to be automatically awarded to users who engage in environmental conservation activities, and their actions can be widely shared, providing continuous motivation.

[0178] "Input means" refers to the means by which a user inputs information into the system, and includes devices such as smartphones and tablets.

[0179] The "data receiving means" is a means for receiving data entered by a user and transferring it to the server.

[0180] "Data analysis means" means means for analyzing received data and understanding and evaluating its content, including generative AI technology and image analysis technology.

[0181] The "point calculation means" is a means for evaluating the user's behavior and calculating points based on the results of the data analysis means.

[0182] The "point granting means" is a means for granting calculated points to a user's account.

[0183] The "notification means" is a means for notifying the user of the point allocation result.

[0184] "Linking means" refers to a means for linking data processed within the system with other social networking services.

[0185] "Storage means" refers to the means for storing and managing all data, including receipt data, analysis results, and point allocation results.

[0186] "Generative AI technology means" means means that use generative AI technology to analyze text or understand its content.

[0187] "Image analysis means" means means that uses image analysis techniques to analyze and evaluate the content of an image.

[0188] "SNS sharing means" refers to a means for automatically posting the content reported by a user to other social networking services.

[0189] This invention is a system that allows users to report their environmental protection activities, award points based on the content of the reports, and share the results on social media. This system is designed to continuously motivate users to participate in environmental protection activities, and is configured as follows:

[0190] System Overview

[0191] 1. User Input Method

[0192] Users can use their smartphones or smart glasses to input text and images about their environmental conservation activities. For example, they can write "I picked up trash in the city today" and attach a photo of themselves picking up trash.

[0193] 2. Means of receiving data

[0194] The text and image data sent from the device is sent over the Internet to a server, which receives and stores this data.

[0195] 3. Data Analysis Methods

[0196] The server analyzes the received data using generative AI (GPT-4) and image analysis technology (TensorFlow). The generative AI technology analyzes the input text and extracts keywords. The image analysis technology checks whether the attached image contains content related to environmental protection activities.

[0197] 4. Point Calculation Method

[0198] Based on the analysis results, the server calculates points corresponding to the user's actions. For example, "picking up trash" is valued at 20 points for environmental protection.

[0199] 5. Points Awarding Method

[0200] The calculated points are added to the user's account, and the user can check the points on their My Page within the app.

[0201] 6. Means of notification

[0202] The server notifies the user of the point allocation result. For example, a message saying "20 points have been allocated" is displayed in the app.

[0203] 7. Social Media Integration Methods

[0204] Depending on the user's settings, the report may also be automatically posted to other social networking services, such as Twitter, allowing users to share their environmental protection activities more widely.

[0205] 8. Preservation means

[0206] The server stores all receipt data, analysis results, and point allocation results, and this data is used for long-term analysis and system management.

[0207] Hardware and software used

[0208] Generative AI model: GPT-4 (used via Hugging Face, etc.)

[0209] Image analysis technology: TensorFlow

[0210] Server: AWS (Amazon Web Services)

[0211] Database: PostgreSQL

[0212] Social media APIs: Twitter API, etc.

[0213] Adding specific examples

[0214] For example, suppose a user uses their smartphone to type the text "I picked up trash on the street today" and attach a photo of themselves picking up trash. The server receives this data and analyzes it using generative AI technology (GPT-4) and image analysis technology (TensorFlow). If the analysis determines that "picking up trash" is worth 20 points, the server will add 20 points to the user's account and notify them of the result. At the same time, this report is automatically posted to Twitter. Finally, all data is stored in a database (PostgreSQL).

[0215] Prompt Sentence Examples

[0216] "Please generate an example program for a security service app that allows users to report environmental protection activities and award points based on those activities. The technologies used will be generative AI (GPT-4) and image analysis (TensorFlow). This application will be installed on smartphones and smart glasses."

[0217] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0218] Step 1:

[0219] Users use their smartphones or smart glasses to input text and images of environmental conservation activities.

[0220] Input: Text (e.g., "I picked up trash in the city today"), image (e.g., a photo of someone picking up trash).

[0221] Output: Input data is sent from the device to the server.

[0222] Step 2:

[0223] The server receives the text and image data sent from the terminal.

[0224] Input: Text and image data entered by the user.

[0225] Output: The server receives the data and stores it for further analysis steps.

[0226] Step 3:

[0227] The server uses the generative AI (GPT-4) to analyze the received text and extract keywords.

[0228] Input: Received text data.

[0229] Data processing: Use GPT-4 to process text using natural language processing and extract keywords.

[0230] Output: A list of keywords (e.g. "picking up trash").

[0231] Step 4:

[0232] The server uses image analysis technology (TensorFlow) to analyze the received image and confirm its contents.

[0233] Input: Received image data.

[0234] Data processing: Use TensorFlow to recognize and verify image content (e.g., litter collection activities).

[0235] Output: Image analysis results (e.g. matching score indicating litter picking).

[0236] Step 5:

[0237] The server calculates points based on the results of text analysis and image analysis.

[0238] Input: Text analysis results and image analysis results.

[0239] Data calculation: Points are calculated based on the type of environmental protection activity and its evaluation.

[0240] Output: Calculated points (e.g. 20 points).

[0241] Step 6:

[0242] The server will then credit the calculated points to the user's account.

[0243] Input: Calculated points, user account information.

[0244] Specific operation: Point update processing to the database.

[0245] Output: User account with points awarded.

[0246] Step 7:

[0247] The server notifies the user of the result of point allocation.

[0248] Input: Point allocation result, user device information.

[0249] What it does: Sends a notification message to the user's smartphone or smart glasses.

[0250] Output: A notification displayed on the user's device (e.g., "20 points awarded").

[0251] Step 8:

[0252] The server posts the user's report to the configured SNS in order to share it socially.

[0253] Input: User's report content (text and image), user's SNS connection settings.

[0254] Specific operation: Post via SNS API.

[0255] Output: Report posted on social media.

[0256] Step 9:

[0257] The server stores and manages all data (received data, analysis results, point allocation results).

[0258] Input: The output data for each step.

[0259] Specific operation: Saving to the database.

[0260] Output: The saved data is stored in a database.

[0261] Furthermore, an emotion engine that estimates the user's emotion may be combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0262] This invention is a system that awards points to users by entering "good deeds for the Earth" into a social networking app. The system incorporates an emotion engine that recognizes the user's emotions, and adjusts the evaluation and rewards of actions according to the user's emotional state. This allows for detailed feedback to be given to users, strengthening their motivation to continue environmental protection activities.

[0263] System configuration

[0264] 1. Input Method

[0265] Users use social media apps to input text and images. For example, they can type "I picked up trash today" and attach a photo of the trash collection. This input method is done on devices such as smartphones and tablets.

[0266] 2. Means of receiving data

[0267] The data transmitted from the terminal is received by the server, which receives the data via the Internet and stores it for analysis.

[0268] 3. Data Analysis Methods

[0269] The server analyzes the received text and images using generative AI technology and image analysis technology. For example, the text analysis module extracts the keyword "litter picking," and the image analysis module verifies that the attached photo shows someone picking up trash.

[0270] 4. Emotion Engine

[0271] The server analyzes the received text and images using an emotion engine to determine the user's emotional state, for example, by determining the user's sense of satisfaction or accomplishment from the context of the text and analyzing the user's facial expression from the image.

[0272] 5. Point Calculation Method

[0273] The server scores the contribution of each behavior based on the results of the data analysis and emotion engine. Points are adjusted according to the user's emotional state. For example, additional points are awarded if positive emotions are expressed.

[0274] 6. Points Awarding Method

[0275] The server will then add the calculated points to the user's account, which the user can view within the app.

[0276] 7. Means of notification

[0277] The server notifies the terminal of the result of point allocation. For example, a message saying "10 points have been allocated" is displayed.

[0278] 8. Collaboration Methods

[0279] The server then shares the user's posts with other social networking services. For example, if the user has enabled it, it can automatically post to Twitter or Facebook, "I picked up trash today."

[0280] 9. Preservation means

[0281] The server stores all received data, analysis results, emotion recognition results, and point allocation results. This data is used for system management and further analysis.

[0282] Specific examples

[0283] A user enters "I used my own bottle today" into a social media app and attaches a photo of the bottle. When this information is entered using a smartphone, the data is sent to a server via the internet. The server analyzes the received data using a generative AI and checks the keyword "I used my own bottle" and the content of the photo. The emotion engine also determines whether the user's text expresses satisfaction. This action is assessed as a contribution of 5 points, and because the emotion is positive, an additional 2 points are awarded. A total of 7 points are awarded to the user's account, and they are notified.

[0284] At the same time, this post is automatically posted to other social media accounts (e.g., Twitter) selected by the user, so that their personal bottle use activities can be widely shared.Finally, the server stores all data and uses it for long-term analysis and management.

[0285] In this way, the system not only motivates users to take sustainable actions to protect the environment and provides a means to widely share their achievements, but also strengthens users' internal motivation through its emotional engine.

[0286] The processing flow will be explained below.

[0287] Step 1:

[0288] A user launches a social media app and accesses the posting form.

[0289] Step 2:

[0290] The user enters the text "I commuted to work by bicycle today" into the posting form and attaches a photo of their bicycle.

[0291] Step 3:

[0292] The user presses the submit button to confirm the input.

[0293] Step 4:

[0294] The terminal packages the input text and image data and sends it to the server.

[0295] Step 5:

[0296] The server receives the data sent from the terminal.

[0297] Step 6:

[0298] The server passes the received data to a text analysis module, which analyzes the text.

[0299] Specific operation: The generative AI analyzes the text and extracts the keyword "bicycle commuting."

[0300] Step 7:

[0301] The server passes the image data to an image analysis module, which analyzes the image.

[0302] What it does: Uses image classification techniques to verify that the attached image is a photo of a bicycle.

[0303] Step 8:

[0304] The server passes the text and image data to an emotion engine to analyze the user's emotional state.

[0305] Specific behavior: The emotion engine determines the user's satisfaction or sense of accomplishment from the context of the text, and identifies the user's emotional state by analyzing their facial expressions from images.

[0306] Step 9:

[0307] The server scores the contribution of the behavior based on the results of text analysis, image analysis, and sentiment analysis.

[0308] Specific behavior: The behavior of "commuting by bicycle" is given a score of 10 points, with an additional 2 points added because it expresses positive emotions.

[0309] Step 10:

[0310] The server references the user's account database and adds the calculated 12 points to the user's account.

[0311] Step 11:

[0312] The server generates a message informing the user of the points being awarded.

[0313] Specific Action: Create a notification that says "A total of 12 points have been awarded."

[0314] Step 12:

[0315] The server generates a notification message and sends it to the terminal.

[0316] Step 13:

[0317] Display notification messages received by the device to the user.

[0318] What happens: A notification will appear in the app saying "You have been awarded a total of 12 points."

[0319] Step 14:

[0320] The server then links the posts to other social networking services based on the user's settings.

[0321] Specific operation: Converts the posted data into a specified format and sends it to Twitter or Facebook.

[0322] Step 15:

[0323] The server stores the received data, analysis results, emotion recognition results, and point allocation results in a database.

[0324] Step 16:

[0325] The server obtains advertising revenue data from the management system and secures the points funds.

[0326] Step 17:

[0327] The server manages a portion of the advertising revenue as a source of points redemption, ensuring the sustainability of the system.

[0328] Example 2

[0329] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0330] While existing social networking apps and environmental protection activity apps have systems for rating users' behavior, they lack a mechanism for recognizing the user's emotional state and reflecting it in the rating. This results in a lack of internal motivation for users, leading to problems with users not continuing to participate for long. Furthermore, there are also insufficient ways to effectively share posts on other social networking services, limiting the effectiveness of publicizing environmental protection activities.

[0331] The specific processing by the specific processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes an input means used by the user for convenience, a data receiving means for receiving input data, a data analysis means for analyzing the received data, an emotion engine means for recognizing the user's emotional state, a point calculation means for calculating points based on the analysis results and the emotion engine results, a point awarding means for awarding the calculated points to the user, a notification means for notifying the user of the awarded points, a linking means for linking data to other social networking services, and a storage means for saving and managing the accumulated data. This enables detailed evaluation and feedback based on the user's emotions, thereby strengthening motivation for environmental protection activities. Furthermore, linking with other social networking services enables widespread sharing and public relations effects.

[0332] "Input means" refers to the device or method by which a user inputs data such as text or images via a social networking app.

[0333] "Data receiving means" refers to the function of a server or system that receives data entered by a user via the Internet.

[0334] "Data Analysis Means" means a system or method that analyzes received data to identify and evaluate user behavior and content.

[0335] "Emotion engine means" refers to a system or module that analyzes and identifies an emotional state from text or images entered by a user.

[0336] "Point calculation means" refers to a system or method that calculates points by evaluating the contribution of a user's actions and emotions based on the results of data analysis and the results of the emotion engine.

[0337] "Points Granting Means" refers to a system or method for granting calculated points to a user's account.

[0338] "Notification means" refers to a method or system for notifying users of the results of points awarded.

[0339] "Integration means" refers to a function that automatically links and shares user posts with other social networking services.

[0340] "Storage means" refers to a system or method for storing and managing data such as receipt data, analysis results, emotion recognition results, and point allocation results.

[0341] This invention is a system that awards points to users who perform environmental protection-related actions by entering the actions into a social networking app. The system incorporates an emotion engine that recognizes the user's emotional state, and the evaluation and rewards for the actions are adjusted according to the emotional state. The results are also shared in cooperation with other social networking services.

[0342] First, users use their smartphones, tablets, or other devices to enter "something good they've done for the Earth" into a social media app. This entry includes both text and images. For example, a user might post, "I picked up trash today," and attach a photo of the trash collection.

[0343] The device then sends the input data over the internet to a server. The server receives the data and analyzes the text and images using generative AI techniques (e.g., GPT-3) and image analysis techniques. For example, the text analysis module extracts the keyword "litter picking," and the image analysis module checks whether the attached photo shows someone picking up trash.

[0344] Additionally, the server uses an emotion engine to identify the user's emotional state, for example, by determining the user's satisfaction or accomplishment from the context of the text, or by analyzing the user's facial expressions from the image.

[0345] Next, the server scores the contribution of the action based on the results of the data analysis means and emotion engine. If a positive emotion is expressed, additional points are awarded. For example, the action of "picking up trash" is evaluated as having a contribution of 5 points, and an additional 2 points are awarded because the emotion is positive. In this way, a total of 7 points are awarded to the user.

[0346] The server will then add the points to the user's account, which the user can check in the app. The server will then notify the device of the points addition result, and the device will display a message to the user saying "7 points have been added."

[0347] In addition, user posts are automatically posted to other social media accounts (e.g., Twitter, Facebook) that have been set up. For example, if a user has set up Twitter integration, the content "I picked up trash today" will be automatically posted.

[0348] Finally, the server stores all received data, analysis results, emotion recognition results, and point allocation results, which are used for subsequent management and analysis.

[0349] For example, if a user types "I used my own bottle today" into a social media app and attaches a photo of their bottle, you might use the following prompt:

[0350] "When a user posts, 'I used my own bottle today,' and attaches a photo, build a system that analyzes the post, identifies keywords and emotions, and awards points."

[0351] "Please explain the system's processing procedure when a specific example of an action is posted: 'Picking up trash.'"

[0352] This system is highly effective because it not only motivates users to take sustainable actions to protect the environment and provides a means for them to widely share their achievements, but also strengthens users' internal motivation through its emotional engine.

[0353] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0354] Step 1:

[0355] Users can use a social networking app to enter text and images about the good things they have done for the Earth. For example, they can post, "I picked up trash today," and attach a photo of the trash collection.

[0356] Input: Text input and image file

[0357] Output: The text "I picked up trash today" is input as data and an image file is generated.

[0358] Step 2:

[0359] The terminal transmits the input data to a server via the Internet.

[0360] Input: User-entered text and image data

[0361] Output: Text and image data are sent to the server.

[0362] Step 3:

[0363] The server receives the data sent by the user.

[0364] Input: Text and image data sent over the internet

[0365] Output: Save the received data to the internal storage.

[0366] Step 4:

[0367] The server analyzes the text data using generative AI techniques, such as GPT-3, to extract keywords like "litter picking" from the text.

[0368] Input: Received text data

[0369] Output: Extracted keyword "litter picking"

[0370] Step 5:

[0371] The server analyzes the received image data using image analysis technology, specifically determining whether the image content shows litter picking.

[0372] Input: Received image data

[0373] Output: Image analysis results (e.g., "Image and confirmation of litter collection")

[0374] Step 6:

[0375] The server uses an emotion engine to identify the user's emotional state from the text and image data, specifically determining feelings of satisfaction and accomplishment.

[0376] Input: Parsed text and image data

[0377] Output: Sentiment analysis result (e.g. "Satisfied")

[0378] Step 7:

[0379] The server scores the contribution of the behavior based on the results of the data analysis means and the emotion engine, and gives additional points if positive emotions are expressed.

[0380] Input: Text analysis results, image analysis results, sentiment analysis results

[0381] Output: Points as score (e.g., 5 base points + 2 additional points for emotional state = 7 total points)

[0382] Step 8:

[0383] The server credits the calculated points to the user's account.

[0384] Input: Calculated points

[0385] Output: Points added to your account

[0386] Step 9:

[0387] The server notifies the terminal of the result of the point allocation.

[0388] Input: Point allocation result

[0389] Output: A notification message on the device saying "7 points awarded"

[0390] Step 10:

[0391] The terminal displays a notification message to the user.

[0392] Input: Notification message from the server

[0393] Output: The notification message displayed to the user

[0394] Step 11:

[0395] The server automatically posts the user's posts to other social networking services (e.g., Twitter, Facebook) that you have set up.

[0396] Input: User posted content and integration settings

[0397] Output: Posts displayed on other social networks

[0398] Step 12:

[0399] The server stores data such as receipt data, analysis results, emotion recognition results, and point allocation results. All data is stored in a database.

[0400] Input: Text data, image data, analysis results, emotional state, point allocation results

[0401] Output: A comprehensive saved dataset

[0402] (Application example 2)

[0403] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0404] Current food delivery services lack incentives for users to make environmentally friendly choices. This means that the use of reusable containers and the selection of zero-emission delivery methods are not sufficiently promoted. Furthermore, there is no system in place to recognize users' positive emotions and provide additional rewards, making it difficult to encourage sustainable environmental protection activities. There is a need to solve this problem and link user behavior to environmental protection.

[0405] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes an input means used by the user for convenience, a data receiving means for receiving input data, a data analysis means for analyzing the received data, a point calculation means for calculating points based on the data analyzed by the data analysis means and the results of the emotion engine, a point awarding means for awarding the calculated points to the user, a notification means for notifying the user of the awarded points, a linking means for linking data to other social networking services, a storage means for saving and managing accumulated data, an emotion engine for analyzing the user's emotional state using emotion recognition technology, and an environmental selection confirmation means for confirming that the user has made an environmentally friendly choice. This makes it possible to adjust rewards according to the user's emotional state and promote behavior that contributes to environmental protection.

[0406] An "input means" is a means by which a user provides information to the system, and is an input device that uses a smartphone or tablet.

[0407] "Data receiving means" refers to a means by which the server receives data input by the user, and has the function of receiving data mainly via the Internet.

[0408] "Data Analysis Means" means means for analyzing received data and interpreting the content of text and images using generative AI techniques and image analysis techniques.

[0409] An "emotion engine" is a technology for analyzing a user's emotional state, recognizing the user's emotions based on text and images.

[0410] The "point calculation means" is a means for calculating points to be awarded to a user based on the data analyzed by the data analysis means and the results of the emotion engine.

[0411] The "point granting means" is a means for granting calculated points to a user, and has the function of reflecting the points in the user's account.

[0412] "Notification means" refers to a means for notifying users of the points they have been awarded, such as sending information via push notification on a smartphone or email.

[0413] "Linking means" refers to a means for linking data to other social networking services, and automatically posting user actions to other platforms.

[0414] "Storage means" refers to the means for storing and managing accumulated data, and is used to later use the data stored on the server for analysis and management.

[0415] An "environmental choice confirmation means" is a means for a user to confirm that they have made an environmentally friendly choice (such as choosing a reusable container or a zero-emission delivery method).

[0416] The present invention provides an application for inputting information about environmentally friendly choices made by a user, and a system for calculating and awarding points by analyzing the data obtained thereby. The system includes an input means used by the user for convenience, a data receiving means for receiving the input data, a data analysis means for analyzing the received data, an emotion engine for analyzing the user's emotional state using emotion recognition technology, a point calculation means for calculating points, a point awarding means for awarding the calculated points to the user, a notification means for notifying the user of the awarded points, a linking means for linking data to other social networking services, a storage means for saving and managing the data, and an environmental selection confirmation means.

[0417] Hardware and software used

[0418] 1. Smartphones and tablets: These are devices where users input information.

[0419] 2. Server: Receives data, analyzes, stores, and notifies.

[0420] 3. Generative AI model: The model used to analyze data.

[0421] 4. Emotion Engine: Technology for analyzing the user's emotional state.

[0422] 5. Image analysis software: Techniques for analyzing the content of images.

[0423] Data processing and calculation

[0424] 1. Input method: Users use their smartphone or tablet to input information about environmentally friendly choices, such as reusable containers or zero-emission delivery methods. For example, if they choose a reusable container, they input the information and associated text and images.

[0425] 2. Data reception method: The input data is sent to the server via the Internet. The server stores the received data for analysis.

[0426] 3. Data analysis means: The server analyzes the received data using generative AI models and image analysis software. For example, the text analysis module extracts keywords such as "reusable containers" and "zero-emission delivery," and the image analysis module checks the content of the attached photos.

[0427] 4. Emotion Engine: The server analyzes the user's emotional state based on the text and images. It determines whether the user is satisfied from the text and identifies the emotion by analyzing the user's facial expression from the image.

[0428] 5. Point calculation method: The server calculates points based on the analysis results and the emotion engine results. For example, if a user selects "reusable container," basic points are awarded, and if a positive emotion is recognized, additional points are awarded.

[0429] 6. Point allocation means: The calculated points are allocated to the user's account by the server.

[0430] 7. Notification method: The server notifies the user of the points awarded. A push notification or email message stating "10 points awarded" is sent.

[0431] 8. Collaboration: The server can be configured to share user posts with other social networking services. For example, a message saying "I used a reusable container today" can be automatically posted to Twitter or Facebook.

[0432] 9. Storage: All received data, analysis results, emotion recognition results, and point allocation results will be stored on the server and used for long-term analysis and management.

[0433] 10. Environmental Choice Verification: The server analyzes information about reusable containers and zero-emission delivery methods to verify that the user has made an environmentally friendly choice.

[0434] Specific examples

[0435] For example, if a user selects a reusable container using a smartphone app and enters the text "I was very satisfied," this information is sent to a server via the Internet. The server then analyzes the text using a generative AI model to extract the keyword "reusable container." The emotion engine then recognizes positive emotions from the text "I was very satisfied." Based on the analysis results, basic points are awarded, and additional points are awarded because positive emotions were recognized. The final calculated points are reflected in the user's account, and a push notification is sent stating, "15 points awarded." If the user has configured this, the post is also automatically shared on social networking services.

[0436] Prompt Sentence Examples

[0437] "Analyze the sentiment of user reviews and rate the degree to which they express positive emotions. Sentence: Today's delivery was fantastic!"

[0438] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0439] Step 1:

[0440] Users input information into the application using a smartphone or tablet. Specifically, users select a reusable container or a zero-emission delivery method, and enter text such as "I was very satisfied." The input data includes text and, if necessary, images. The input data is sent to the endpoint via the Internet. The input data includes "order ID," "eco-selection," "delivery method," "user review," and "user image."

[0441] Step 2:

[0442] The server receives data sent from the input means using the data receiving means. The received data is stored in a database and is then analyzed. The input here is text and image data sent by the user, and the output is in the saved data format.

[0443] Step 3:

[0444] The server begins analyzing the received data using data analysis means. Specifically, it uses a generative AI model to analyze the text data and extract keywords related to eco-friendly choices. For example, keywords such as "reusable containers" and "zero-emission delivery" are extracted. The image data is analyzed using image analysis software to verify its content. The input is the stored data, and the output is the analysis results.

[0445] Step 4:

[0446] The emotion engine analyzes the emotional state of a user from text and images. In text analysis, positive emotions are recognized from expressions such as "I am very satisfied." In image analysis, emotions are identified from the user's facial expressions. The input is text and image data, and the output is an emotion score.

[0447] Step 5:

[0448] The server calculates points using a point calculation means based on the results of the data analysis means and the emotion engine. Base points are awarded according to the eco-friendly choices made by the user. In addition, additional points are calculated based on the emotion score. For example, selecting "reusable containers" earns 10 points, and positive emotions earn an additional 5 points. The inputs are the analysis results and the emotion score, and the output is the calculated points.

[0449] Step 6:

[0450] Using the point awarding means, the calculated points are awarded to the user's account. The server stores this information in a database. The input is the calculated points, and the output is the points reflected in the user's account.

[0451] Step 7:

[0452] Using the notification method, the server notifies the user of the points awarded. It sends a push notification or email notification with a message saying "15 points awarded." The input is the points reflected in the user's account, and the output is the notification message sent.

[0453] Step 8:

[0454] By using a linking mechanism, if the user selects it, the server automatically posts the data to other social networking services. For example, a message such as "I used a reusable container today" is posted to Twitter or Facebook. The input is the user's selected data, and the output is a post to another social networking service.

[0455] Step 9:

[0456] The server stores and manages all received data, analysis results, emotion recognition results, and point allocation results. The stored data is used for long-term analysis and management. The input is all analysis result data, and the output is the stored data.

[0457] Step 10:

[0458] Using the environmental choice confirmation method, the server confirms that the user has made an environmentally friendly choice (e.g., choosing a reusable container or a zero-emission delivery method). The server then verifies the environmentally friendly choice by comparing it with the analysis results. The input is the user's selection data, and the output is the confirmation result.

[0459] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0460] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0461] In the above embodiment, an example in which the specific process is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific process may be performed by the smart device 14.

[0462] [Second embodiment]

[0463] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0464] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0465] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0466] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0467] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0468] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0469] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0470] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0471] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific 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.

[0472] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0473] In the smart glasses 214, the reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. 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 process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0474] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. 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."

[0475] This invention is a system that awards points to users by entering "good deeds for the Earth" into a social networking app. This system provides continuous motivation for users to continue environmental protection activities easily, share their actions, and receive rewards.

[0476] System configuration

[0477] 1. Input Method

[0478] Users use social media apps to input text and images. For example, they might write, "I commuted by bicycle today," and attach a photo of their bicycle. This input method is done on devices such as smartphones and tablets.

[0479] 2. Means of receiving data

[0480] The data transmitted from the terminal is received by the server, which receives the data via the Internet and stores it for analysis.

[0481] 3. Data Analysis Methods

[0482] The server analyzes the received text and images using AI generative technology and image analysis technology. For example, the text analysis module extracts the keyword "bicycle commuting," and the image analysis module verifies that the attached photo is of a bicycle.

[0483] 4. Point Calculation Method

[0484] The server calculates the contribution of an action based on the results of the data analysis means, for example, "commuting by bicycle" is evaluated as being worth 10 points to environmental protection.

[0485] 5. Points Awarding Method

[0486] The server will then calculate the points and credit them to the user's account, which the user can view within the app.

[0487] 6. Means of notification

[0488] The server notifies the terminal of the result of point allocation. For example, a message saying "10 points have been allocated" is displayed.

[0489] 7. Collaboration Methods

[0490] The server then shares the user's posts with other social networking services. For example, if the user has enabled it, it can automatically post to Twitter or Facebook, "I commuted by bicycle today."

[0491] 8. Preservation means

[0492] The server stores all receipt data, analysis results, and point allocation results. This data is used for system management and further analysis.

[0493] Specific examples

[0494] A user enters "I used my own bottle today" into a social media app and attaches a photo. When this information is entered using a smartphone, the data is sent to a server via the internet. The server analyzes the received data using generative AI and checks the keyword "I used my own bottle" and the content of the photo. If this action is evaluated as contributing 5 points to environmental protection, the server will add 5 points to the user's account and notify them of this.

[0495] At the same time, this post is automatically posted to other social media accounts (e.g., Twitter) selected by the user, so that their personal bottle use activities can be widely shared.Finally, the server stores all data and uses it for long-term analysis and management.

[0496] In this way, the system provides users with an incentive to take sustainable actions to protect the environment and a means to widely share the results.

[0497] The processing flow will be explained below.

[0498] Step 1:

[0499] A user launches a social media app and accesses the posting form.

[0500] Step 2:

[0501] The user enters the text "I commuted to work by bicycle today" into the posting form and attaches a photo of their bicycle.

[0502] Step 3:

[0503] The user presses the submit button to confirm the input.

[0504] Step 4:

[0505] The terminal packages the input text and image data and sends it to the server.

[0506] Step 5:

[0507] The server receives the data sent from the terminal.

[0508] Step 6:

[0509] The server passes the received data to a text analysis module, which analyzes the text.

[0510] Specific operation: The generative AI analyzes the text and extracts the keyword "bicycle commuting."

[0511] Step 7:

[0512] The server passes the image data to an image analysis module, which analyzes the image.

[0513] What it does: Uses image classification techniques to verify that the attached image is a photo of a bicycle.

[0514] Step 8:

[0515] The server scores the contribution of the behavior based on the results of text analysis and image analysis.

[0516] Specific action: The action of "commuting by bicycle" is given 10 points.

[0517] Step 9:

[0518] The server references the user's account database and adds the calculated 10 points to the user's account.

[0519] Step 10:

[0520] The server generates a message informing the user of the points being awarded.

[0521] Specific action: Create a notification that says "10 points awarded."

[0522] Step 11:

[0523] The server generates a notification message and sends it to the terminal.

[0524] Step 12:

[0525] Display notification messages received by the device to the user.

[0526] What happens: A notification will appear in the app saying "10 points awarded."

[0527] Step 13:

[0528] The server then links the posts to other social networking services based on the user's settings.

[0529] Specific operation: Converts the posted data into a specified format and sends it to Twitter or Facebook.

[0530] Step 14:

[0531] The server stores the receipt data, analysis results, and point allocation results in a database.

[0532] Step 15:

[0533] The server obtains advertising revenue data from the management system and secures the points funds.

[0534] Step 16:

[0535] The server manages a portion of the advertising revenue as a source of points redemption, ensuring the sustainability of the system.

[0536] Example 1

[0537] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0538] Currently, there are only a limited number of systems that promote environmental protection activities, and most of them have difficulty providing continuous motivation to users. Furthermore, there is a lack of systems that analyze the accuracy of input information, appropriately evaluate it, and provide rewards. Furthermore, these systems are rarely linked to other online platforms and are rarely widely recognized, which poses the challenge of lacking continuous motivation.

[0539] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0540] In this invention, the server includes an input means used by users for operation, a data receiving means for receiving input information, a data analysis means for analyzing the received information, a point calculation means for calculating points based on the analysis results, a point awarding means for awarding the calculated points to users, a notification means for notifying the users of the awarded points, a linking means for linking the information to other online platforms, and a storage means for saving and managing the accumulated information. This allows users to easily record their environmental protection activities, widely share those activities, and receive continuous motivation.

[0541] "User" refers to the entity that performs operations or inputs, and includes individuals and organizations.

[0542] "Input means" refers to a device or method for a user to input information such as text and images.

[0543] "Data receiving means" refers to a means for receiving input information and transmitting it to a server.

[0544] "Data Analysis Tools" refers to techniques and devices for analyzing received text and images.

[0545] "Point calculation means" refers to the technology or device used to evaluate user behavior and calculate points based on the results of data analysis.

[0546] "Points Granting Means" refers to the means by which calculated points are added to a User's account.

[0547] "Notification means" refers to the technology or device that notifies users of the results of point allocation.

[0548] "Linkage means" refers to the technology or device that allows users to share their input information and activities with other online platforms.

[0549] "Storage means" refers to the technology and devices used to store and manage received information, analysis results, and point allocation results.

[0550] "Artificial intelligence technology" refers to the technology of analyzing input text using techniques such as machine learning, deep learning, and natural language processing.

[0551] "Image analysis technology" refers to technology for analyzing input images and recognizing their contents.

[0552] "Online platform" refers to services and websites provided via the Internet, including social networking sites and information sharing services.

[0553] This invention is a system that awards points to users by entering "good deeds for the Earth" into a social networking app. This system provides continuous motivation for users to continue environmental protection activities easily, share their actions, and receive rewards.

[0554] First, users access a social networking app using a device such as a smartphone or tablet. The application provides an input method for users to enter their environmental conservation activities using text and images. For example, a user might enter, "I commuted to work by bicycle today," and attach a photo of their bicycle.

[0555] The data entered by the user through the input means is sent to the server via the data receiving means. The data sent from the terminal arrives at the server via the Internet and is temporarily stored in a database.

[0556] The server then uses data analysis tools to analyze the received text and images. Specifically, it combines generative AI technology (artificial intelligence technology) and image analysis technology to perform text analysis and image recognition. For example, the text analysis module extracts the keyword "bicycle commuting," and the image analysis module recognizes bicycles from the attached photo.

[0557] After the analysis is completed, the server uses a point calculation means to evaluate the contribution of the behavior based on the analysis results and calculate points. For example, "commuting by bicycle" is evaluated as being worth 10 points for environmental protection. This evaluation standard is preset in the system.

[0558] Once the points are calculated, the server activates a points granting means to grant the points to the user's account. The calculated points are reflected in the user's account information. The user can check the current number of points through the application.

[0559] The result of point allocation will be notified to the user through a notification method. Specifically, a message saying "10 points have been allocated" will be sent to the user's smartphone in real time.

[0560] Furthermore, through the integration method, users' posts about environmental protection activities can be automatically shared on other social networking sites (online platforms). For example, if the user has set it up in advance, the post can also be linked and shared on Twitter, Facebook, etc. This allows the user's activities to be widely recognized.

[0561] Finally, the server uses storage means to store the receipt data, analysis results, point allocation results, etc. for a long period of time. This data is used for system management and further analysis.

[0562] Specific examples

[0563] A user types "I used my own bottle today" into a social media app and attaches a photo. This data is sent from the device to a server via the internet. The server uses generative AI technology to analyze the received data and checks the keyword "I used my own bottle" and the content of the photo. This action is assessed as contributing 5 points to environmental protection, and 5 points are awarded to the user's account. The user is also notified of the result, and depending on their settings, it is automatically posted to other social media platforms such as Twitter. Finally, the server stores all data and uses it for long-term analysis and management.

[0564] Prompt Sentence Examples

[0565] "When a user enters their environmental protection actions into a social media app, the server analyzes the text and images using generative AI technology, calculates and awards points, and notifies the user of the results, and shares the post on other social media platforms. Please explain with a concrete example."

[0566] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0567] Step 1:

[0568] A user launches a social networking app and inputs text and images. For example, they might type, "I commuted by bicycle today," and attach a photo of their bicycle. Input is done using a device such as a smartphone or tablet.

[0569] Input: Text and images entered by the user.

[0570] Output: Data packets that the device sends to the server.

[0571] Specific behavior: A user enters text into an app's input field and attaches an image.

[0572] Step 2:

[0573] The terminal transmits the input data to a server via the Internet.

[0574] Input: Text and images entered by the user.

[0575] Output: The data sent to the server.

[0576] Specific operation: The terminal packetizes the data and sends an HTTP request. The data is encrypted and sent.

[0577] Step 3:

[0578] The server temporarily stores the data received from the terminal in a database.

[0579] Input: Data sent from the terminal.

[0580] Output: Data stored in the database.

[0581] Specific operation: The server parses the received JSON format data and issues an INSERT statement to the database.

[0582] Step 4:

[0583] The server analyzes the received text and images using generative AI technology and image analysis technology.

[0584] Input: Text and image data stored in a database.

[0585] Output: Parsed keywords and verification results.

[0586] How it works: The generative AI model analyzes the text and extracts the keyword "bicycle commuting." Image analysis technology recognizes bicycles from images.

[0587] Step 5:

[0588] The server calculates the contribution of the behavior based on the results of the data analysis, and calculates points.

[0589] Input: Parsed keywords and verification results.

[0590] Output: The calculated points.

[0591] What happens: The server calculates points using predefined rating logic. "Commuting by bicycle" is rated as 10 points.

[0592] Step 6:

[0593] The server credits the calculated points to the user's account.

[0594] Input: The calculated point.

[0595] Output: Points added to the user's account information.

[0596] Specific operation: The server executes an SQL query to update the user's account information and add new points to the current points.

[0597] Step 7:

[0598] The server notifies the terminal of the result of the point allocation.

[0599] Input: Point award results.

[0600] Output: Notification message to user terminal.

[0601] Specific operation: The server sends a push notification using a real-time notification service (e.g., Firebase Cloud Messaging).

[0602] Step 8:

[0603] The server then links the user's posts to other online platforms.

[0604] Input: User posts and collaboration settings.

[0605] Output: Posting to other online platforms.

[0606] Specific operation: The server uses OAuth to access the user's social media account and sends an API request to automatically post the configured content to Twitter or Facebook, for example.

[0607] Step 9:

[0608] The server stores and manages the receipt data, analysis results, and point allocation results over the long term.

[0609] Input: Received data, analysis results, point allocation results.

[0610] Output: Saved data.

[0611] Specific operation: The server issues an INSERT statement to the database to permanently store various data. The stored data can be used for future analysis and system audits.

[0612] Through these steps, users can record their environmental protection actions, receive points for their actions, and share them more widely, providing lasting motivation.

[0613] (Application example 1)

[0614] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0615] There is a need for a system that can sustainably encourage the efforts of individuals who engage in environmental protection activities and effectively share their actions. Conventional systems simply aggregate the environmental protection activities performed by users, without providing specific rewards or widespread sharing, making it difficult to maintain individual motivation. Furthermore, there is a lack of means to objectively evaluate the effectiveness of environmental protection activities. To solve these issues, a system is needed that automatically analyzes users' reports and provides motivation for continuous environmental protection activities through points awarding and sharing on social media.

[0616] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0617] In this invention, the server includes an input means used by users for convenience, a data receiving means for receiving input data, a data analysis means for analyzing the received data, a point calculation means for calculating points based on the analysis results, a point awarding means for awarding the calculated points to users, a notification means for notifying the users of the awarded points, a linking means for linking data to other social networking services, a storage means for saving and managing accumulated data, a generation AI technology means for using generation AI technology as data analysis means, an image analysis means for using image analysis technology as image analysis means, and an SNS sharing means for sharing posts on SNS based on the user's reports. This allows points to be automatically awarded to users who engage in environmental conservation activities, and their actions can be widely shared, providing continuous motivation.

[0618] "Input means" refers to the means by which a user inputs information into the system, and includes devices such as smartphones and tablets.

[0619] The "data receiving means" is a means for receiving data entered by a user and transferring it to the server.

[0620] "Data analysis means" means means for analyzing received data and understanding and evaluating its content, including generative AI technology and image analysis technology.

[0621] The "point calculation means" is a means for evaluating the user's behavior and calculating points based on the results of the data analysis means.

[0622] The "point granting means" is a means for granting calculated points to a user's account.

[0623] The "notification means" is a means for notifying the user of the point allocation result.

[0624] "Linking means" refers to a means for linking data processed within the system with other social networking services.

[0625] "Storage means" refers to the means for storing and managing all data, including receipt data, analysis results, and point allocation results.

[0626] "Generative AI technology means" means means that use generative AI technology to analyze text or understand its content.

[0627] "Image analysis means" means means that uses image analysis techniques to analyze and evaluate the content of an image.

[0628] "SNS sharing means" refers to a means for automatically posting the content reported by a user to other social networking services.

[0629] This invention is a system that allows users to report their environmental protection activities, award points based on the content of the reports, and share the results on social media. This system is designed to continuously motivate users to participate in environmental protection activities, and is configured as follows:

[0630] System Overview

[0631] 1. User Input Method

[0632] Users can use their smartphones or smart glasses to input text and images about their environmental conservation activities. For example, they can write "I picked up trash in the city today" and attach a photo of themselves picking up trash.

[0633] 2. Means of receiving data

[0634] The text and image data sent from the device is sent over the Internet to a server, which receives and stores this data.

[0635] 3. Data Analysis Methods

[0636] The server analyzes the received data using generative AI (GPT-4) and image analysis technology (TensorFlow). The generative AI technology analyzes the input text and extracts keywords. The image analysis technology checks whether the attached image contains content related to environmental protection activities.

[0637] 4. Point Calculation Method

[0638] Based on the analysis results, the server calculates points corresponding to the user's actions. For example, "picking up trash" is valued at 20 points for environmental protection.

[0639] 5. Points Awarding Method

[0640] The calculated points are added to the user's account, and the user can check the points on their My Page within the app.

[0641] 6. Means of notification

[0642] The server notifies the user of the point allocation result. For example, a message saying "20 points have been allocated" is displayed in the app.

[0643] 7. Social Media Integration Methods

[0644] Depending on the user's settings, the report may also be automatically posted to other social networking services, such as Twitter, allowing users to share their environmental protection activities more widely.

[0645] 8. Preservation means

[0646] The server stores all receipt data, analysis results, and point allocation results, and this data is used for long-term analysis and system management.

[0647] Hardware and software used

[0648] Generative AI model: GPT-4 (used via Hugging Face, etc.)

[0649] Image analysis technology: TensorFlow

[0650] Server: AWS (Amazon Web Services)

[0651] Database: PostgreSQL

[0652] Social media APIs: Twitter API, etc.

[0653] Adding specific examples

[0654] For example, suppose a user uses their smartphone to type the text "I picked up trash on the street today" and attach a photo of themselves picking up trash. The server receives this data and analyzes it using generative AI technology (GPT-4) and image analysis technology (TensorFlow). If the analysis determines that "picking up trash" is worth 20 points, the server will add 20 points to the user's account and notify them of the result. At the same time, this report is automatically posted to Twitter. Finally, all data is stored in a database (PostgreSQL).

[0655] Prompt Sentence Examples

[0656] "Please generate an example program for a security service app that allows users to report environmental protection activities and award points based on those activities. The technologies used will be generative AI (GPT-4) and image analysis (TensorFlow). This application will be installed on smartphones and smart glasses."

[0657] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0658] Step 1:

[0659] Users use their smartphones or smart glasses to input text and images of environmental conservation activities.

[0660] Input: Text (e.g., "I picked up trash in the city today"), image (e.g., a photo of someone picking up trash).

[0661] Output: Input data is sent from the device to the server.

[0662] Step 2:

[0663] The server receives the text and image data sent from the terminal.

[0664] Input: Text and image data entered by the user.

[0665] Output: The server receives the data and stores it for further analysis steps.

[0666] Step 3:

[0667] The server uses the generative AI (GPT-4) to analyze the received text and extract keywords.

[0668] Input: Received text data.

[0669] Data processing: Use GPT-4 to process text using natural language processing and extract keywords.

[0670] Output: A list of keywords (e.g. "picking up trash").

[0671] Step 4:

[0672] The server uses image analysis technology (TensorFlow) to analyze the received image and confirm its contents.

[0673] Input: Received image data.

[0674] Data processing: Use TensorFlow to recognize and verify image content (e.g., litter collection activities).

[0675] Output: Image analysis results (e.g. matching score indicating litter picking).

[0676] Step 5:

[0677] The server calculates points based on the results of text analysis and image analysis.

[0678] Input: Text analysis results and image analysis results.

[0679] Data calculation: Points are calculated based on the type of environmental protection activity and its evaluation.

[0680] Output: Calculated points (e.g. 20 points).

[0681] Step 6:

[0682] The server will then credit the calculated points to the user's account.

[0683] Input: Calculated points, user account information.

[0684] Specific operation: Point update processing to the database.

[0685] Output: User account with points awarded.

[0686] Step 7:

[0687] The server notifies the user of the result of point allocation.

[0688] Input: Point allocation result, user device information.

[0689] What it does: Sends a notification message to the user's smartphone or smart glasses.

[0690] Output: A notification displayed on the user's device (e.g., "20 points awarded").

[0691] Step 8:

[0692] The server posts the user's report to the configured SNS in order to share it socially.

[0693] Input: User's report content (text and image), user's SNS connection settings.

[0694] Specific operation: Post via SNS API.

[0695] Output: Report posted on social media.

[0696] Step 9:

[0697] The server stores and manages all data (received data, analysis results, point allocation results).

[0698] Input: The output data for each step.

[0699] Specific operation: Saving to the database.

[0700] Output: The saved data is stored in a database.

[0701] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[0702] This invention is a system that awards points to users by entering "good deeds for the Earth" into a social networking app. The system incorporates an emotion engine that recognizes the user's emotions, and adjusts the evaluation and rewards of actions according to the user's emotional state. This allows for detailed feedback to be given to users, strengthening their motivation to continue environmental protection activities.

[0703] System configuration

[0704] 1. Input Method

[0705] Users use social media apps to input text and images. For example, they can type "I picked up trash today" and attach a photo of the trash collection. This input method is done on devices such as smartphones and tablets.

[0706] 2. Means of receiving data

[0707] The data transmitted from the terminal is received by the server, which receives the data via the Internet and stores it for analysis.

[0708] 3. Data Analysis Methods

[0709] The server analyzes the received text and images using generative AI technology and image analysis technology. For example, the text analysis module extracts the keyword "litter picking," and the image analysis module verifies that the attached photo shows someone picking up trash.

[0710] 4. Emotion Engine

[0711] The server analyzes the received text and images using an emotion engine to determine the user's emotional state, for example, by determining the user's sense of satisfaction or accomplishment from the context of the text and analyzing the user's facial expression from the image.

[0712] 5. Point Calculation Method

[0713] The server scores the contribution of each behavior based on the results of the data analysis and emotion engine. Points are adjusted according to the user's emotional state. For example, additional points are awarded if positive emotions are expressed.

[0714] 6. Points Awarding Method

[0715] The server will then add the calculated points to the user's account, which the user can view within the app.

[0716] 7. Means of notification

[0717] The server notifies the terminal of the result of point allocation. For example, a message saying "10 points have been allocated" is displayed.

[0718] 8. Collaboration Methods

[0719] The server then shares the user's posts with other social networking services. For example, if the user has enabled it, it can automatically post to Twitter or Facebook, "I picked up trash today."

[0720] 9. Preservation means

[0721] The server stores all received data, analysis results, emotion recognition results, and point allocation results. This data is used for system management and further analysis.

[0722] Specific examples

[0723] A user enters "I used my own bottle today" into a social media app and attaches a photo of the bottle. When this information is entered using a smartphone, the data is sent to a server via the internet. The server analyzes the received data using a generative AI and checks the keyword "I used my own bottle" and the content of the photo. The emotion engine also determines whether the user's text expresses satisfaction. This action is assessed as a contribution of 5 points, and because the emotion is positive, an additional 2 points are awarded. A total of 7 points are awarded to the user's account, and they are notified.

[0724] At the same time, this post is automatically posted to other social media accounts (e.g., Twitter) selected by the user, so that their personal bottle use activities can be widely shared.Finally, the server stores all data and uses it for long-term analysis and management.

[0725] In this way, the system not only motivates users to take sustainable actions to protect the environment and provides a means to widely share their achievements, but also strengthens users' internal motivation through its emotional engine.

[0726] The processing flow will be explained below.

[0727] Step 1:

[0728] A user launches a social media app and accesses the posting form.

[0729] Step 2:

[0730] The user enters the text "I commuted to work by bicycle today" into the posting form and attaches a photo of their bicycle.

[0731] Step 3:

[0732] The user presses the submit button to confirm the input.

[0733] Step 4:

[0734] The terminal packages the input text and image data and sends it to the server.

[0735] Step 5:

[0736] The server receives the data sent from the terminal.

[0737] Step 6:

[0738] The server passes the received data to a text analysis module, which analyzes the text.

[0739] Specific operation: The generative AI analyzes the text and extracts the keyword "bicycle commuting."

[0740] Step 7:

[0741] The server passes the image data to an image analysis module, which analyzes the image.

[0742] What it does: Uses image classification techniques to verify that the attached image is a photo of a bicycle.

[0743] Step 8:

[0744] The server passes the text and image data to an emotion engine to analyze the user's emotional state.

[0745] Specific behavior: The emotion engine determines the user's satisfaction or sense of accomplishment from the context of the text, and identifies the user's emotional state by analyzing their facial expressions from images.

[0746] Step 9:

[0747] The server scores the contribution of the behavior based on the results of text analysis, image analysis, and sentiment analysis.

[0748] Specific behavior: The behavior of "commuting by bicycle" is given a score of 10 points, with an additional 2 points added because it expresses positive emotions.

[0749] Step 10:

[0750] The server references the user's account database and adds the calculated 12 points to the user's account.

[0751] Step 11:

[0752] The server generates a message informing the user of the points being awarded.

[0753] Specific Action: Create a notification that says "A total of 12 points have been awarded."

[0754] Step 12:

[0755] The server generates a notification message and sends it to the terminal.

[0756] Step 13:

[0757] Display notification messages received by the device to the user.

[0758] What happens: A notification will appear in the app saying "You have been awarded a total of 12 points."

[0759] Step 14:

[0760] The server then links the posts to other social networking services based on the user's settings.

[0761] Specific operation: Converts the posted data into a specified format and sends it to Twitter or Facebook.

[0762] Step 15:

[0763] The server stores the received data, analysis results, emotion recognition results, and point allocation results in a database.

[0764] Step 16:

[0765] The server obtains advertising revenue data from the management system and secures the points funds.

[0766] Step 17:

[0767] The server manages a portion of the advertising revenue as a source of points redemption, ensuring the sustainability of the system.

[0768] Example 2

[0769] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0770] While existing social networking apps and environmental protection activity apps have systems for rating users' behavior, they lack a mechanism for recognizing the user's emotional state and reflecting it in the rating. This results in a lack of internal motivation for users, leading to problems with users not continuing to participate for long. Furthermore, there are also insufficient ways to effectively share posts on other social networking services, limiting the effectiveness of publicizing environmental protection activities.

[0771] The specific processing by the specific processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes an input means used by the user for convenience, a data receiving means for receiving input data, a data analysis means for analyzing the received data, an emotion engine means for recognizing the user's emotional state, a point calculation means for calculating points based on the analysis results and the emotion engine results, a point awarding means for awarding the calculated points to the user, a notification means for notifying the user of the awarded points, a linking means for linking data to other social networking services, and a storage means for saving and managing the accumulated data. This enables detailed evaluation and feedback based on the user's emotions, thereby strengthening motivation for environmental protection activities. Furthermore, linking with other social networking services enables widespread sharing and public relations effects.

[0772] "Input means" refers to the device or method by which a user inputs data such as text or images via a social networking app.

[0773] "Data receiving means" refers to the function of a server or system that receives data entered by a user via the Internet.

[0774] "Data Analysis Means" means a system or method that analyzes received data to identify and evaluate user behavior and content.

[0775] "Emotion engine means" refers to a system or module that analyzes and identifies an emotional state from text or images entered by a user.

[0776] "Point calculation means" refers to a system or method that calculates points by evaluating the contribution of a user's actions and emotions based on the results of data analysis and the results of the emotion engine.

[0777] "Points Granting Means" refers to a system or method for granting calculated points to a user's account.

[0778] "Notification means" refers to a method or system for notifying users of the results of points awarded.

[0779] "Integration means" refers to a function that automatically links and shares user posts with other social networking services.

[0780] "Storage means" refers to a system or method for storing and managing data such as receipt data, analysis results, emotion recognition results, and point allocation results.

[0781] This invention is a system that awards points to users who perform environmental protection-related actions by entering the actions into a social networking app. The system incorporates an emotion engine that recognizes the user's emotional state, and the evaluation and rewards for the actions are adjusted according to the emotional state. The results are also shared in cooperation with other social networking services.

[0782] First, users use their smartphones, tablets, or other devices to enter "something good they've done for the Earth" into a social media app. This entry includes both text and images. For example, a user might post, "I picked up trash today," and attach a photo of the trash collection.

[0783] The device then sends the input data over the internet to a server. The server receives the data and analyzes the text and images using generative AI techniques (e.g., GPT-3) and image analysis techniques. For example, the text analysis module extracts the keyword "litter picking," and the image analysis module checks whether the attached photo shows someone picking up trash.

[0784] Additionally, the server uses an emotion engine to identify the user's emotional state, for example, by determining the user's satisfaction or accomplishment from the context of the text, or by analyzing the user's facial expressions from the image.

[0785] Next, the server scores the contribution of the action based on the results of the data analysis means and emotion engine. If a positive emotion is expressed, additional points are awarded. For example, the action of "picking up trash" is evaluated as having a contribution of 5 points, and an additional 2 points are awarded because the emotion is positive. In this way, a total of 7 points are awarded to the user.

[0786] The server will then add the points to the user's account, which the user can check in the app. The server will then notify the device of the points addition result, and the device will display a message to the user saying "7 points have been added."

[0787] In addition, user posts are automatically posted to other social media accounts (e.g., Twitter, Facebook) that have been set up. For example, if a user has set up Twitter integration, the content "I picked up trash today" will be automatically posted.

[0788] Finally, the server stores all received data, analysis results, emotion recognition results, and point allocation results, which are used for subsequent management and analysis.

[0789] For example, if a user types "I used my own bottle today" into a social media app and attaches a photo of their bottle, you might use the following prompt:

[0790] "When a user posts, 'I used my own bottle today,' and attaches a photo, build a system that analyzes the post, identifies keywords and emotions, and awards points."

[0791] "Please explain the system's processing procedure when a specific example of an action is posted: 'Picking up trash.'"

[0792] This system is highly effective because it not only motivates users to take sustainable actions to protect the environment and provides a means for them to widely share their achievements, but also strengthens users' internal motivation through its emotional engine.

[0793] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0794] Step 1:

[0795] Users can use a social networking app to enter text and images about the good things they have done for the Earth. For example, they can post, "I picked up trash today," and attach a photo of the trash collection.

[0796] Input: Text input and image file

[0797] Output: The text "I picked up trash today" is input as data and an image file is generated.

[0798] Step 2:

[0799] The terminal transmits the input data to a server via the Internet.

[0800] Input: User-entered text and image data

[0801] Output: Text and image data are sent to the server.

[0802] Step 3:

[0803] The server receives the data sent by the user.

[0804] Input: Text and image data sent over the internet

[0805] Output: Save the received data to the internal storage.

[0806] Step 4:

[0807] The server analyzes the text data using generative AI techniques, such as GPT-3, to extract keywords like "litter picking" from the text.

[0808] Input: Received text data

[0809] Output: Extracted keyword "litter picking"

[0810] Step 5:

[0811] The server analyzes the received image data using image analysis technology, specifically determining whether the image content shows litter picking.

[0812] Input: Received image data

[0813] Output: Image analysis results (e.g., "Image and confirmation of litter collection")

[0814] Step 6:

[0815] The server uses an emotion engine to identify the user's emotional state from the text and image data, specifically determining feelings of satisfaction and accomplishment.

[0816] Input: Parsed text and image data

[0817] Output: Sentiment analysis result (e.g. "Satisfied")

[0818] Step 7:

[0819] The server scores the contribution of the behavior based on the results of the data analysis means and the emotion engine, and gives additional points if positive emotions are expressed.

[0820] Input: Text analysis results, image analysis results, sentiment analysis results

[0821] Output: Points as score (e.g., 5 base points + 2 additional points for emotional state = 7 total points)

[0822] Step 8:

[0823] The server credits the calculated points to the user's account.

[0824] Input: Calculated points

[0825] Output: Points added to your account

[0826] Step 9:

[0827] The server notifies the terminal of the result of the point allocation.

[0828] Input: Point allocation result

[0829] Output: A notification message on the device saying "7 points awarded"

[0830] Step 10:

[0831] The terminal displays a notification message to the user.

[0832] Input: Notification message from the server

[0833] Output: The notification message displayed to the user

[0834] Step 11:

[0835] The server automatically posts the user's posts to other social networking services (e.g., Twitter, Facebook) that you have set up.

[0836] Input: User posted content and integration settings

[0837] Output: Posts displayed on other social networks

[0838] Step 12:

[0839] The server stores data such as receipt data, analysis results, emotion recognition results, and point allocation results. All data is stored in a database.

[0840] Input: Text data, image data, analysis results, emotional state, point allocation results

[0841] Output: A comprehensive saved dataset

[0842] (Application example 2)

[0843] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0844] Current food delivery services lack incentives for users to make environmentally friendly choices. This means that the use of reusable containers and the selection of zero-emission delivery methods are not sufficiently promoted. Furthermore, there is no system in place to recognize users' positive emotions and provide additional rewards, making it difficult to encourage sustainable environmental protection activities. There is a need to solve this problem and link user behavior to environmental protection.

[0845] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes an input means used by the user for convenience, a data receiving means for receiving input data, a data analysis means for analyzing the received data, a point calculation means for calculating points based on the data analyzed by the data analysis means and the results of the emotion engine, a point awarding means for awarding the calculated points to the user, a notification means for notifying the user of the awarded points, a linking means for linking data to other social networking services, a storage means for saving and managing accumulated data, an emotion engine for analyzing the user's emotional state using emotion recognition technology, and an environmental selection confirmation means for confirming that the user has made an environmentally friendly choice. This makes it possible to adjust rewards according to the user's emotional state and promote behavior that contributes to environmental protection.

[0846] An "input means" is a means by which a user provides information to the system, and is an input device that uses a smartphone or tablet.

[0847] "Data receiving means" refers to a means by which the server receives data input by the user, and has the function of receiving data mainly via the Internet.

[0848] "Data Analysis Means" means means for analyzing received data and interpreting the content of text and images using generative AI techniques and image analysis techniques.

[0849] An "emotion engine" is a technology for analyzing a user's emotional state, recognizing the user's emotions based on text and images.

[0850] The "point calculation means" is a means for calculating points to be awarded to a user based on the data analyzed by the data analysis means and the results of the emotion engine.

[0851] The "point granting means" is a means for granting calculated points to a user, and has the function of reflecting the points in the user's account.

[0852] "Notification means" refers to a means for notifying users of the points they have been awarded, such as sending information via push notification on a smartphone or email.

[0853] "Linking means" refers to a means for linking data to other social networking services, and automatically posting user actions to other platforms.

[0854] "Storage means" refers to the means for storing and managing accumulated data, and is used to later use the data stored on the server for analysis and management.

[0855] An "environmental choice confirmation means" is a means for a user to confirm that they have made an environmentally friendly choice (such as choosing a reusable container or a zero-emission delivery method).

[0856] The present invention provides an application for inputting information about environmentally friendly choices made by a user, and a system for calculating and awarding points by analyzing the data obtained thereby. The system includes an input means used by the user for convenience, a data receiving means for receiving the input data, a data analysis means for analyzing the received data, an emotion engine for analyzing the user's emotional state using emotion recognition technology, a point calculation means for calculating points, a point awarding means for awarding the calculated points to the user, a notification means for notifying the user of the awarded points, a linking means for linking data to other social networking services, a storage means for saving and managing the data, and an environmental selection confirmation means.

[0857] Hardware and software used

[0858] 1. Smartphones and tablets: These are devices where users input information.

[0859] 2. Server: Receives data, analyzes, stores, and notifies.

[0860] 3. Generative AI model: The model used to analyze data.

[0861] 4. Emotion Engine: Technology for analyzing the user's emotional state.

[0862] 5. Image analysis software: Techniques for analyzing the content of images.

[0863] Data processing and calculation

[0864] 1. Input method: Users use their smartphone or tablet to input information about environmentally friendly choices, such as reusable containers or zero-emission delivery methods. For example, if they choose a reusable container, they input the information and associated text and images.

[0865] 2. Data reception method: The input data is sent to the server via the Internet. The server stores the received data for analysis.

[0866] 3. Data analysis means: The server analyzes the received data using generative AI models and image analysis software. For example, the text analysis module extracts keywords such as "reusable containers" and "zero-emission delivery," and the image analysis module checks the content of the attached photos.

[0867] 4. Emotion Engine: The server analyzes the user's emotional state based on the text and images. It determines whether the user is satisfied from the text and identifies the emotion by analyzing the user's facial expression from the image.

[0868] 5. Point calculation method: The server calculates points based on the analysis results and the emotion engine results. For example, if a user selects "reusable container," basic points are awarded, and if a positive emotion is recognized, additional points are awarded.

[0869] 6. Point allocation means: The calculated points are allocated to the user's account by the server.

[0870] 7. Notification method: The server notifies the user of the points awarded. A push notification or email message stating "10 points awarded" is sent.

[0871] 8. Collaboration: The server can be configured to share user posts with other social networking services. For example, a message saying "I used a reusable container today" can be automatically posted to Twitter or Facebook.

[0872] 9. Storage: All received data, analysis results, emotion recognition results, and point allocation results will be stored on the server and used for long-term analysis and management.

[0873] 10. Environmental Choice Verification: The server analyzes information about reusable containers and zero-emission delivery methods to verify that the user has made an environmentally friendly choice.

[0874] Specific examples

[0875] For example, if a user selects a reusable container using a smartphone app and enters the text "I was very satisfied," this information is sent to a server via the Internet. The server then analyzes the text using a generative AI model to extract the keyword "reusable container." The emotion engine then recognizes positive emotions from the text "I was very satisfied." Based on the analysis results, basic points are awarded, and additional points are awarded because positive emotions were recognized. The final calculated points are reflected in the user's account, and a push notification is sent stating, "15 points awarded." If the user has configured this, the post is also automatically shared on social networking services.

[0876] Prompt Sentence Examples

[0877] "Analyze the sentiment of user reviews and rate the degree to which they express positive emotions. Sentence: Today's delivery was fantastic!"

[0878] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0879] Step 1:

[0880] Users input information into the application using a smartphone or tablet. Specifically, users select a reusable container or a zero-emission delivery method, and enter text such as "I was very satisfied." The input data includes text and, if necessary, images. The input data is sent to the endpoint via the Internet. The input data includes "order ID," "eco-selection," "delivery method," "user review," and "user image."

[0881] Step 2:

[0882] The server receives data sent from the input means using the data receiving means. The received data is stored in a database and is then analyzed. The input here is text and image data sent by the user, and the output is in the saved data format.

[0883] Step 3:

[0884] The server begins analyzing the received data using data analysis means. Specifically, it uses a generative AI model to analyze the text data and extract keywords related to eco-friendly choices. For example, keywords such as "reusable containers" and "zero-emission delivery" are extracted. The image data is analyzed using image analysis software to verify its content. The input is the stored data, and the output is the analysis results.

[0885] Step 4:

[0886] The emotion engine analyzes the emotional state of a user from text and images. In text analysis, positive emotions are recognized from expressions such as "I am very satisfied." In image analysis, emotions are identified from the user's facial expressions. The input is text and image data, and the output is an emotion score.

[0887] Step 5:

[0888] The server calculates points using a point calculation means based on the results of the data analysis means and the emotion engine. Base points are awarded according to the eco-friendly choices made by the user. In addition, additional points are calculated based on the emotion score. For example, selecting "reusable containers" earns 10 points, and positive emotions earn an additional 5 points. The inputs are the analysis results and the emotion score, and the output is the calculated points.

[0889] Step 6:

[0890] Using the point awarding means, the calculated points are awarded to the user's account. The server stores this information in a database. The input is the calculated points, and the output is the points reflected in the user's account.

[0891] Step 7:

[0892] Using the notification method, the server notifies the user of the points awarded. It sends a push notification or email notification with a message saying "15 points awarded." The input is the points reflected in the user's account, and the output is the notification message sent.

[0893] Step 8:

[0894] By using a linking mechanism, if the user selects it, the server automatically posts the data to other social networking services. For example, a message such as "I used a reusable container today" is posted to Twitter or Facebook. The input is the user's selected data, and the output is a post to another social networking service.

[0895] Step 9:

[0896] The server stores and manages all received data, analysis results, emotion recognition results, and point allocation results. The stored data is used for long-term analysis and management. The input is all analysis result data, and the output is the stored data.

[0897] Step 10:

[0898] Using the environmental choice confirmation method, the server confirms that the user has made an environmentally friendly choice (e.g., choosing a reusable container or a zero-emission delivery method). The server then verifies the environmentally friendly choice by comparing it with the analysis results. The input is the user's selection data, and the output is the confirmation result.

[0899] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0900] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0901] In the above embodiment, an example in which the specific processing is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the smart glasses 214.

[0902] [Third embodiment]

[0903] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0904] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0905] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0906] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0907] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0908] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0909] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0910] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0911] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific 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.

[0912] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0913] In the headset type terminal 314, a reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. 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 process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0914] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. 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."

[0915] This invention is a system that awards points to users by entering "good deeds for the Earth" into a social networking app. This system provides continuous motivation for users to continue environmental protection activities easily, share their actions, and receive rewards.

[0916] System configuration

[0917] 1. Input Method

[0918] Users use social media apps to input text and images. For example, they might write, "I commuted by bicycle today," and attach a photo of their bicycle. This input method is done on devices such as smartphones and tablets.

[0919] 2. Means of receiving data

[0920] The data transmitted from the terminal is received by the server, which receives the data via the Internet and stores it for analysis.

[0921] 3. Data Analysis Methods

[0922] The server analyzes the received text and images using AI generative technology and image analysis technology. For example, the text analysis module extracts the keyword "bicycle commuting," and the image analysis module verifies that the attached photo is of a bicycle.

[0923] 4. Point Calculation Method

[0924] The server calculates the contribution of an action based on the results of the data analysis means, for example, "commuting by bicycle" is evaluated as being worth 10 points to environmental protection.

[0925] 5. Points Awarding Method

[0926] The server will then calculate the points and credit them to the user's account, which the user can view within the app.

[0927] 6. Means of notification

[0928] The server notifies the terminal of the result of point allocation. For example, a message saying "10 points have been allocated" is displayed.

[0929] 7. Collaboration Methods

[0930] The server then shares the user's posts with other social networking services. For example, if the user has enabled it, it can automatically post to Twitter or Facebook, "I commuted by bicycle today."

[0931] 8. Preservation means

[0932] The server stores all receipt data, analysis results, and point allocation results. This data is used for system management and further analysis.

[0933] Specific examples

[0934] A user enters "I used my own bottle today" into a social media app and attaches a photo. When this information is entered using a smartphone, the data is sent to a server via the internet. The server analyzes the received data using generative AI and checks the keyword "I used my own bottle" and the content of the photo. If this action is evaluated as contributing 5 points to environmental protection, the server will add 5 points to the user's account and notify them of this.

[0935] At the same time, this post is automatically posted to other social media accounts (e.g., Twitter) selected by the user, so that their personal bottle use activities can be widely shared.Finally, the server stores all data and uses it for long-term analysis and management.

[0936] In this way, the system provides users with an incentive to take sustainable actions to protect the environment and a means to widely share the results.

[0937] The processing flow will be explained below.

[0938] Step 1:

[0939] A user launches a social media app and accesses the posting form.

[0940] Step 2:

[0941] The user enters the text "I commuted to work by bicycle today" into the posting form and attaches a photo of their bicycle.

[0942] Step 3:

[0943] The user presses the submit button to confirm the input.

[0944] Step 4:

[0945] The terminal packages the input text and image data and sends it to the server.

[0946] Step 5:

[0947] The server receives the data sent from the terminal.

[0948] Step 6:

[0949] The server passes the received data to a text analysis module, which analyzes the text.

[0950] Specific operation: The generative AI analyzes the text and extracts the keyword "bicycle commuting."

[0951] Step 7:

[0952] The server passes the image data to an image analysis module, which analyzes the image.

[0953] What it does: Uses image classification techniques to verify that the attached image is a photo of a bicycle.

[0954] Step 8:

[0955] The server scores the contribution of the behavior based on the results of text analysis and image analysis.

[0956] Specific action: The action of "commuting by bicycle" is given 10 points.

[0957] Step 9:

[0958] The server references the user's account database and adds the calculated 10 points to the user's account.

[0959] Step 10:

[0960] The server generates a message informing the user of the points being awarded.

[0961] Specific action: Create a notification that says "10 points awarded."

[0962] Step 11:

[0963] The server generates a notification message and sends it to the terminal.

[0964] Step 12:

[0965] Display notification messages received by the device to the user.

[0966] What happens: A notification will appear in the app saying "10 points awarded."

[0967] Step 13:

[0968] The server then links the posts to other social networking services based on the user's settings.

[0969] Specific operation: Converts the posted data into a specified format and sends it to Twitter or Facebook.

[0970] Step 14:

[0971] The server stores the receipt data, analysis results, and point allocation results in a database.

[0972] Step 15:

[0973] The server obtains advertising revenue data from the management system and secures the points funds.

[0974] Step 16:

[0975] The server manages a portion of the advertising revenue as a source of points redemption, ensuring the sustainability of the system.

[0976] Example 1

[0977] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[0978] Currently, there are only a limited number of systems that promote environmental protection activities, and most of them have difficulty providing continuous motivation to users. Furthermore, there is a lack of systems that analyze the accuracy of input information, appropriately evaluate it, and provide rewards. Furthermore, these systems are rarely linked to other online platforms and are rarely widely recognized, which poses the challenge of lacking continuous motivation.

[0979] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0980] In this invention, the server includes an input means used by users for operation, a data receiving means for receiving input information, a data analysis means for analyzing the received information, a point calculation means for calculating points based on the analysis results, a point awarding means for awarding the calculated points to users, a notification means for notifying the users of the awarded points, a linking means for linking the information to other online platforms, and a storage means for saving and managing the accumulated information. This allows users to easily record their environmental protection activities, widely share those activities, and receive continuous motivation.

[0981] "User" refers to the entity that performs operations or inputs, and includes individuals and organizations.

[0982] "Input means" refers to a device or method for a user to input information such as text and images.

[0983] "Data receiving means" refers to a means for receiving input information and transmitting it to a server.

[0984] "Data Analysis Tools" refers to techniques and devices for analyzing received text and images.

[0985] "Point calculation means" refers to the technology or device used to evaluate user behavior and calculate points based on the results of data analysis.

[0986] "Points Granting Means" refers to the means by which calculated points are added to a User's account.

[0987] "Notification means" refers to the technology or device that notifies users of the results of point allocation.

[0988] "Linkage means" refers to the technology or device that allows users to share their input information and activities with other online platforms.

[0989] "Storage means" refers to the technology and devices used to store and manage received information, analysis results, and point allocation results.

[0990] "Artificial intelligence technology" refers to the technology of analyzing input text using techniques such as machine learning, deep learning, and natural language processing.

[0991] "Image analysis technology" refers to technology for analyzing input images and recognizing their contents.

[0992] "Online platform" refers to services and websites provided via the Internet, including social networking sites and information sharing services.

[0993] This invention is a system that awards points to users by entering "good deeds for the Earth" into a social networking app. This system provides continuous motivation for users to continue environmental protection activities easily, share their actions, and receive rewards.

[0994] First, users access a social networking app using a device such as a smartphone or tablet. The application provides an input method for users to enter their environmental conservation activities using text and images. For example, a user might enter, "I commuted to work by bicycle today," and attach a photo of their bicycle.

[0995] The data entered by the user through the input means is sent to the server via the data receiving means. The data sent from the terminal arrives at the server via the Internet and is temporarily stored in a database.

[0996] The server then uses data analysis tools to analyze the received text and images. Specifically, it combines generative AI technology (artificial intelligence technology) and image analysis technology to perform text analysis and image recognition. For example, the text analysis module extracts the keyword "bicycle commuting," and the image analysis module recognizes bicycles from the attached photo.

[0997] After the analysis is completed, the server uses a point calculation means to evaluate the contribution of the behavior based on the analysis results and calculate points. For example, "commuting by bicycle" is evaluated as being worth 10 points for environmental protection. This evaluation standard is preset in the system.

[0998] Once the points are calculated, the server activates a points granting means to grant the points to the user's account. The calculated points are reflected in the user's account information. The user can check the current number of points through the application.

[0999] The result of point allocation will be notified to the user through a notification method. Specifically, a message saying "10 points have been allocated" will be sent to the user's smartphone in real time.

[1000] Furthermore, through the integration method, users' posts about environmental protection activities can be automatically shared on other social networking sites (online platforms). For example, if the user has set it up in advance, the post can also be linked and shared on Twitter, Facebook, etc. This allows the user's activities to be widely recognized.

[1001] Finally, the server uses storage means to store the receipt data, analysis results, point allocation results, etc. for a long period of time. This data is used for system management and further analysis.

[1002] Specific examples

[1003] A user types "I used my own bottle today" into a social media app and attaches a photo. This data is sent from the device to a server via the internet. The server uses generative AI technology to analyze the received data and checks the keyword "I used my own bottle" and the content of the photo. This action is assessed as contributing 5 points to environmental protection, and 5 points are awarded to the user's account. The user is also notified of the result, and depending on their settings, it is automatically posted to other social media platforms such as Twitter. Finally, the server stores all data and uses it for long-term analysis and management.

[1004] Prompt Sentence Examples

[1005] "When a user enters their environmental protection actions into a social media app, the server analyzes the text and images using generative AI technology, calculates and awards points, and notifies the user of the results, and shares the post on other social media platforms. Please explain with a concrete example."

[1006] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1007] Step 1:

[1008] A user launches a social networking app and inputs text and images. For example, they might type, "I commuted by bicycle today," and attach a photo of their bicycle. Input is done using a device such as a smartphone or tablet.

[1009] Input: Text and images entered by the user.

[1010] Output: Data packets that the device sends to the server.

[1011] Specific behavior: A user enters text into an app's input field and attaches an image.

[1012] Step 2:

[1013] The terminal transmits the input data to a server via the Internet.

[1014] Input: Text and images entered by the user.

[1015] Output: The data sent to the server.

[1016] Specific operation: The terminal packetizes the data and sends an HTTP request. The data is encrypted and sent.

[1017] Step 3:

[1018] The server temporarily stores the data received from the terminal in a database.

[1019] Input: Data sent from the terminal.

[1020] Output: Data stored in the database.

[1021] Specific operation: The server parses the received JSON format data and issues an INSERT statement to the database.

[1022] Step 4:

[1023] The server analyzes the received text and images using generative AI technology and image analysis technology.

[1024] Input: Text and image data stored in a database.

[1025] Output: Parsed keywords and verification results.

[1026] How it works: The generative AI model analyzes the text and extracts the keyword "bicycle commuting." Image analysis technology recognizes bicycles from images.

[1027] Step 5:

[1028] The server calculates the contribution of the behavior based on the results of the data analysis, and calculates points.

[1029] Input: Parsed keywords and verification results.

[1030] Output: The calculated points.

[1031] What happens: The server calculates points using predefined rating logic. "Commuting by bicycle" is rated as 10 points.

[1032] Step 6:

[1033] The server credits the calculated points to the user's account.

[1034] Input: The calculated point.

[1035] Output: Points added to the user's account information.

[1036] Specific operation: The server executes an SQL query to update the user's account information and add new points to the current points.

[1037] Step 7:

[1038] The server notifies the terminal of the result of the point allocation.

[1039] Input: Point award results.

[1040] Output: Notification message to user terminal.

[1041] Specific operation: The server sends a push notification using a real-time notification service (e.g., Firebase Cloud Messaging).

[1042] Step 8:

[1043] The server then links the user's posts to other online platforms.

[1044] Input: User posts and collaboration settings.

[1045] Output: Posting to other online platforms.

[1046] Specific operation: The server uses OAuth to access the user's social media account and sends an API request to automatically post the configured content to Twitter or Facebook, for example.

[1047] Step 9:

[1048] The server stores and manages the receipt data, analysis results, and point allocation results over the long term.

[1049] Input: Received data, analysis results, point allocation results.

[1050] Output: Saved data.

[1051] Specific operation: The server issues an INSERT statement to the database to permanently store various data. The stored data can be used for future analysis and system audits.

[1052] Through these steps, users can record their environmental protection actions, receive points for their actions, and share them more widely, providing lasting motivation.

[1053] (Application example 1)

[1054] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1055] There is a need for a system that can sustainably encourage the efforts of individuals who engage in environmental protection activities and effectively share their actions. Conventional systems simply aggregate the environmental protection activities performed by users, without providing specific rewards or widespread sharing, making it difficult to maintain individual motivation. Furthermore, there is a lack of means to objectively evaluate the effectiveness of environmental protection activities. To solve these issues, a system is needed that automatically analyzes users' reports and provides motivation for continuous environmental protection activities through points awarding and sharing on social media.

[1056] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[1057] In this invention, the server includes an input means used by users for convenience, a data receiving means for receiving input data, a data analysis means for analyzing the received data, a point calculation means for calculating points based on the analysis results, a point awarding means for awarding the calculated points to users, a notification means for notifying the users of the awarded points, a linking means for linking data to other social networking services, a storage means for saving and managing accumulated data, a generation AI technology means for using generation AI technology as data analysis means, an image analysis means for using image analysis technology as image analysis means, and an SNS sharing means for sharing posts on SNS based on the user's reports. This allows points to be automatically awarded to users who engage in environmental conservation activities, and their actions can be widely shared, providing continuous motivation.

[1058] "Input means" refers to the means by which a user inputs information into the system, and includes devices such as smartphones and tablets.

[1059] The "data receiving means" is a means for receiving data entered by a user and transferring it to the server.

[1060] "Data analysis means" means means for analyzing received data and understanding and evaluating its content, including generative AI technology and image analysis technology.

[1061] The "point calculation means" is a means for evaluating the user's behavior and calculating points based on the results of the data analysis means.

[1062] The "point granting means" is a means for granting calculated points to a user's account.

[1063] The "notification means" is a means for notifying the user of the point allocation result.

[1064] "Linking means" refers to a means for linking data processed within the system with other social networking services.

[1065] "Storage means" refers to the means for storing and managing all data, including receipt data, analysis results, and point allocation results.

[1066] "Generative AI technology means" means means that use generative AI technology to analyze text or understand its content.

[1067] "Image analysis means" means means that uses image analysis techniques to analyze and evaluate the content of an image.

[1068] "SNS sharing means" refers to a means for automatically posting the content reported by a user to other social networking services.

[1069] This invention is a system that allows users to report their environmental protection activities, award points based on the content of the reports, and share the results on social media. This system is designed to continuously motivate users to participate in environmental protection activities, and is configured as follows:

[1070] System Overview

[1071] 1. User Input Method

[1072] Users can use their smartphones or smart glasses to input text and images about their environmental conservation activities. For example, they can write "I picked up trash in the city today" and attach a photo of themselves picking up trash.

[1073] 2. Means of receiving data

[1074] The text and image data sent from the device is sent over the Internet to a server, which receives and stores this data.

[1075] 3. Data Analysis Methods

[1076] The server analyzes the received data using generative AI (GPT-4) and image analysis technology (TensorFlow). The generative AI technology analyzes the input text and extracts keywords. The image analysis technology checks whether the attached image contains content related to environmental protection activities.

[1077] 4. Point Calculation Method

[1078] Based on the analysis results, the server calculates points corresponding to the user's actions. For example, "picking up trash" is valued at 20 points for environmental protection.

[1079] 5. Points Awarding Method

[1080] The calculated points are added to the user's account, and the user can check the points on their My Page within the app.

[1081] 6. Means of notification

[1082] The server notifies the user of the point allocation result. For example, a message saying "20 points have been allocated" is displayed in the app.

[1083] 7. Social Media Integration Methods

[1084] Depending on the user's settings, the report may also be automatically posted to other social networking services, such as Twitter, allowing users to share their environmental protection activities more widely.

[1085] 8. Preservation means

[1086] The server stores all receipt data, analysis results, and point allocation results, and this data is used for long-term analysis and system management.

[1087] Hardware and software used

[1088] Generative AI model: GPT-4 (used via Hugging Face, etc.)

[1089] Image analysis technology: TensorFlow

[1090] Server: AWS (Amazon Web Services)

[1091] Database: PostgreSQL

[1092] Social media APIs: Twitter API, etc.

[1093] Adding specific examples

[1094] For example, suppose a user uses their smartphone to type the text "I picked up trash on the street today" and attach a photo of themselves picking up trash. The server receives this data and analyzes it using generative AI technology (GPT-4) and image analysis technology (TensorFlow). If the analysis determines that "picking up trash" is worth 20 points, the server will add 20 points to the user's account and notify them of the result. At the same time, this report is automatically posted to Twitter. Finally, all data is stored in a database (PostgreSQL).

[1095] Prompt Sentence Examples

[1096] "Please generate an example program for a security service app that allows users to report environmental protection activities and award points based on those activities. The technologies used will be generative AI (GPT-4) and image analysis (TensorFlow). This application will be installed on smartphones and smart glasses."

[1097] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1098] Step 1:

[1099] Users use their smartphones or smart glasses to input text and images of environmental conservation activities.

[1100] Input: Text (e.g., "I picked up trash in the city today"), image (e.g., a photo of someone picking up trash).

[1101] Output: Input data is sent from the device to the server.

[1102] Step 2:

[1103] The server receives the text and image data sent from the terminal.

[1104] Input: Text and image data entered by the user.

[1105] Output: The server receives the data and stores it for further analysis steps.

[1106] Step 3:

[1107] The server uses the generative AI (GPT-4) to analyze the received text and extract keywords.

[1108] Input: Received text data.

[1109] Data processing: Use GPT-4 to process text using natural language processing and extract keywords.

[1110] Output: A list of keywords (e.g. "picking up trash").

[1111] Step 4:

[1112] The server uses image analysis technology (TensorFlow) to analyze the received image and confirm its contents.

[1113] Input: Received image data.

[1114] Data processing: Use TensorFlow to recognize and verify image content (e.g., litter collection activities).

[1115] Output: Image analysis results (e.g. matching score indicating litter picking).

[1116] Step 5:

[1117] The server calculates points based on the results of text analysis and image analysis.

[1118] Input: Text analysis results and image analysis results.

[1119] Data calculation: Points are calculated based on the type of environmental protection activity and its evaluation.

[1120] Output: Calculated points (e.g. 20 points).

[1121] Step 6:

[1122] The server will then credit the calculated points to the user's account.

[1123] Input: Calculated points, user account information.

[1124] Specific operation: Point update processing to the database.

[1125] Output: User account with points awarded.

[1126] Step 7:

[1127] The server notifies the user of the result of point allocation.

[1128] Input: Point allocation result, user device information.

[1129] What it does: Sends a notification message to the user's smartphone or smart glasses.

[1130] Output: A notification displayed on the user's device (e.g., "20 points awarded").

[1131] Step 8:

[1132] The server posts the user's report to the configured SNS in order to share it socially.

[1133] Input: User's report content (text and image), user's SNS connection settings.

[1134] Specific operation: Post via SNS API.

[1135] Output: Report posted on social media.

[1136] Step 9:

[1137] The server stores and manages all data (received data, analysis results, point allocation results).

[1138] Input: The output data for each step.

[1139] Specific operation: Saving to the database.

[1140] Output: The saved data is stored in a database.

[1141] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1142] This invention is a system that awards points to users by entering "good deeds for the Earth" into a social networking app. The system incorporates an emotion engine that recognizes the user's emotions, and adjusts the evaluation and rewards of actions according to the user's emotional state. This allows for detailed feedback to be given to users, strengthening their motivation to continue environmental protection activities.

[1143] System configuration

[1144] 1. Input Method

[1145] Users use social media apps to input text and images. For example, they can type "I picked up trash today" and attach a photo of the trash collection. This input method is done on devices such as smartphones and tablets.

[1146] 2. Means of receiving data

[1147] The data transmitted from the terminal is received by the server, which receives the data via the Internet and stores it for analysis.

[1148] 3. Data Analysis Methods

[1149] The server analyzes the received text and images using generative AI technology and image analysis technology. For example, the text analysis module extracts the keyword "litter picking," and the image analysis module verifies that the attached photo shows someone picking up trash.

[1150] 4. Emotion Engine

[1151] The server analyzes the received text and images using an emotion engine to determine the user's emotional state, for example, by determining the user's sense of satisfaction or accomplishment from the context of the text and analyzing the user's facial expression from the image.

[1152] 5. Point Calculation Method

[1153] The server scores the contribution of each behavior based on the results of the data analysis and emotion engine. Points are adjusted according to the user's emotional state. For example, additional points are awarded if positive emotions are expressed.

[1154] 6. Points Awarding Method

[1155] The server will then add the calculated points to the user's account, which the user can view within the app.

[1156] 7. Means of notification

[1157] The server notifies the terminal of the result of point allocation. For example, a message saying "10 points have been allocated" is displayed.

[1158] 8. Collaboration Methods

[1159] The server then shares the user's posts with other social networking services. For example, if the user has enabled it, it can automatically post to Twitter or Facebook, "I picked up trash today."

[1160] 9. Preservation means

[1161] The server stores all received data, analysis results, emotion recognition results, and point allocation results. This data is used for system management and further analysis.

[1162] Specific examples

[1163] A user enters "I used my own bottle today" into a social media app and attaches a photo of the bottle. When this information is entered using a smartphone, the data is sent to a server via the internet. The server analyzes the received data using a generative AI and checks the keyword "I used my own bottle" and the content of the photo. The emotion engine also determines whether the user's text expresses satisfaction. This action is assessed as a contribution of 5 points, and because the emotion is positive, an additional 2 points are awarded. A total of 7 points are awarded to the user's account, and they are notified.

[1164] At the same time, this post is automatically posted to other social media accounts (e.g., Twitter) selected by the user, so that their personal bottle use activities can be widely shared.Finally, the server stores all data and uses it for long-term analysis and management.

[1165] In this way, the system not only motivates users to take sustainable actions to protect the environment and provides a means to widely share their achievements, but also strengthens users' internal motivation through its emotional engine.

[1166] The processing flow will be explained below.

[1167] Step 1:

[1168] A user launches a social media app and accesses the posting form.

[1169] Step 2:

[1170] The user enters the text "I commuted to work by bicycle today" into the posting form and attaches a photo of their bicycle.

[1171] Step 3:

[1172] The user presses the submit button to confirm the input.

[1173] Step 4:

[1174] The terminal packages the input text and image data and sends it to the server.

[1175] Step 5:

[1176] The server receives the data sent from the terminal.

[1177] Step 6:

[1178] The server passes the received data to a text analysis module, which analyzes the text.

[1179] Specific operation: The generative AI analyzes the text and extracts the keyword "bicycle commuting."

[1180] Step 7:

[1181] The server passes the image data to an image analysis module, which analyzes the image.

[1182] What it does: Uses image classification techniques to verify that the attached image is a photo of a bicycle.

[1183] Step 8:

[1184] The server passes the text and image data to an emotion engine to analyze the user's emotional state.

[1185] Specific behavior: The emotion engine determines the user's satisfaction or sense of accomplishment from the context of the text, and identifies the user's emotional state by analyzing their facial expressions from images.

[1186] Step 9:

[1187] The server scores the contribution of the behavior based on the results of text analysis, image analysis, and sentiment analysis.

[1188] Specific behavior: The behavior of "commuting by bicycle" is given a score of 10 points, with an additional 2 points added because it expresses positive emotions.

[1189] Step 10:

[1190] The server references the user's account database and adds the calculated 12 points to the user's account.

[1191] Step 11:

[1192] The server generates a message informing the user of the points being awarded.

[1193] Specific Action: Create a notification that says "A total of 12 points have been awarded."

[1194] Step 12:

[1195] The server generates a notification message and sends it to the terminal.

[1196] Step 13:

[1197] Display notification messages received by the device to the user.

[1198] What happens: A notification will appear in the app saying "You have been awarded a total of 12 points."

[1199] Step 14:

[1200] The server then links the posts to other social networking services based on the user's settings.

[1201] Specific operation: Converts the posted data into a specified format and sends it to Twitter or Facebook.

[1202] Step 15:

[1203] The server stores the received data, analysis results, emotion recognition results, and point allocation results in a database.

[1204] Step 16:

[1205] The server obtains advertising revenue data from the management system and secures the points funds.

[1206] Step 17:

[1207] The server manages a portion of the advertising revenue as a source of points redemption, ensuring the sustainability of the system.

[1208] Example 2

[1209] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1210] While existing social networking apps and environmental protection activity apps have systems for rating users' behavior, they lack a mechanism for recognizing the user's emotional state and reflecting it in the rating. This results in a lack of internal motivation for users, leading to problems with users not continuing to participate for long. Furthermore, there are also insufficient ways to effectively share posts on other social networking services, limiting the effectiveness of publicizing environmental protection activities.

[1211] The specific processing by the specific processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes an input means used by the user for convenience, a data receiving means for receiving input data, a data analysis means for analyzing the received data, an emotion engine means for recognizing the user's emotional state, a point calculation means for calculating points based on the analysis results and the emotion engine results, a point awarding means for awarding the calculated points to the user, a notification means for notifying the user of the awarded points, a linking means for linking data to other social networking services, and a storage means for saving and managing the accumulated data. This enables detailed evaluation and feedback based on the user's emotions, thereby strengthening motivation for environmental protection activities. Furthermore, linking with other social networking services enables widespread sharing and public relations effects.

[1212] "Input means" refers to the device or method by which a user inputs data such as text or images via a social networking app.

[1213] "Data receiving means" refers to the function of a server or system that receives data entered by a user via the Internet.

[1214] "Data Analysis Means" means a system or method that analyzes received data to identify and evaluate user behavior and content.

[1215] "Emotion engine means" refers to a system or module that analyzes and identifies an emotional state from text or images entered by a user.

[1216] "Point calculation means" refers to a system or method that calculates points by evaluating the contribution of a user's actions and emotions based on the results of data analysis and the results of the emotion engine.

[1217] "Points Granting Means" refers to a system or method for granting calculated points to a user's account.

[1218] "Notification means" refers to a method or system for notifying users of the results of points awarded.

[1219] "Integration means" refers to a function that automatically links and shares user posts with other social networking services.

[1220] "Storage means" refers to a system or method for storing and managing data such as receipt data, analysis results, emotion recognition results, and point allocation results.

[1221] This invention is a system that awards points to users who perform environmental protection-related actions by entering the actions into a social networking app. The system incorporates an emotion engine that recognizes the user's emotional state, and the evaluation and rewards for the actions are adjusted according to the emotional state. The results are also shared in cooperation with other social networking services.

[1222] First, users use their smartphones, tablets, or other devices to enter "something good they've done for the Earth" into a social media app. This entry includes both text and images. For example, a user might post, "I picked up trash today," and attach a photo of the trash collection.

[1223] The device then sends the input data over the internet to a server. The server receives the data and analyzes the text and images using generative AI techniques (e.g., GPT-3) and image analysis techniques. For example, the text analysis module extracts the keyword "litter picking," and the image analysis module checks whether the attached photo shows someone picking up trash.

[1224] Additionally, the server uses an emotion engine to identify the user's emotional state, for example, by determining the user's satisfaction or accomplishment from the context of the text, or by analyzing the user's facial expressions from the image.

[1225] Next, the server scores the contribution of the action based on the results of the data analysis means and emotion engine. If a positive emotion is expressed, additional points are awarded. For example, the action of "picking up trash" is evaluated as having a contribution of 5 points, and an additional 2 points are awarded because the emotion is positive. In this way, a total of 7 points are awarded to the user.

[1226] The server will then add the points to the user's account, which the user can check in the app. The server will then notify the device of the points addition result, and the device will display a message to the user saying "7 points have been added."

[1227] In addition, user posts are automatically posted to other social media accounts (e.g., Twitter, Facebook) that have been set up. For example, if a user has set up Twitter integration, the content "I picked up trash today" will be automatically posted.

[1228] Finally, the server stores all received data, analysis results, emotion recognition results, and point allocation results, which are used for subsequent management and analysis.

[1229] For example, if a user types "I used my own bottle today" into a social media app and attaches a photo of their bottle, you might use the following prompt:

[1230] "When a user posts, 'I used my own bottle today,' and attaches a photo, build a system that analyzes the post, identifies keywords and emotions, and awards points."

[1231] "Please explain the system's processing procedure when a specific example of an action is posted: 'Picking up trash.'"

[1232] This system is highly effective because it not only motivates users to take sustainable actions to protect the environment and provides a means for them to widely share their achievements, but also strengthens users' internal motivation through its emotional engine.

[1233] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1234] Step 1:

[1235] Users can use a social networking app to enter text and images about the good things they have done for the Earth. For example, they can post, "I picked up trash today," and attach a photo of the trash collection.

[1236] Input: Text input and image file

[1237] Output: The text "I picked up trash today" is input as data and an image file is generated.

[1238] Step 2:

[1239] The terminal transmits the input data to a server via the Internet.

[1240] Input: User-entered text and image data

[1241] Output: Text and image data are sent to the server.

[1242] Step 3:

[1243] The server receives the data sent by the user.

[1244] Input: Text and image data sent over the internet

[1245] Output: Save the received data to the internal storage.

[1246] Step 4:

[1247] The server analyzes the text data using generative AI techniques, such as GPT-3, to extract keywords like "litter picking" from the text.

[1248] Input: Received text data

[1249] Output: Extracted keyword "litter picking"

[1250] Step 5:

[1251] The server analyzes the received image data using image analysis technology, specifically determining whether the image content shows litter picking.

[1252] Input: Received image data

[1253] Output: Image analysis results (e.g., "Image and confirmation of litter collection")

[1254] Step 6:

[1255] The server uses an emotion engine to identify the user's emotional state from the text and image data, specifically determining feelings of satisfaction and accomplishment.

[1256] Input: Parsed text and image data

[1257] Output: Sentiment analysis result (e.g. "Satisfied")

[1258] Step 7:

[1259] The server scores the contribution of the behavior based on the results of the data analysis means and the emotion engine, and gives additional points if positive emotions are expressed.

[1260] Input: Text analysis results, image analysis results, sentiment analysis results

[1261] Output: Points as score (e.g., 5 base points + 2 additional points for emotional state = 7 total points)

[1262] Step 8:

[1263] The server credits the calculated points to the user's account.

[1264] Input: Calculated points

[1265] Output: Points added to your account

[1266] Step 9:

[1267] The server notifies the terminal of the result of the point allocation.

[1268] Input: Point allocation result

[1269] Output: A notification message on the device saying "7 points awarded"

[1270] Step 10:

[1271] The terminal displays a notification message to the user.

[1272] Input: Notification message from the server

[1273] Output: The notification message displayed to the user

[1274] Step 11:

[1275] The server automatically posts the user's posts to other social networking services (e.g., Twitter, Facebook) that you have set up.

[1276] Input: User posted content and integration settings

[1277] Output: Posts displayed on other social networks

[1278] Step 12:

[1279] The server stores data such as receipt data, analysis results, emotion recognition results, and point allocation results. All data is stored in a database.

[1280] Input: Text data, image data, analysis results, emotional state, point allocation results

[1281] Output: A comprehensive saved dataset

[1282] (Application example 2)

[1283] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1284] Current food delivery services lack incentives for users to make environmentally friendly choices. This means that the use of reusable containers and the selection of zero-emission delivery methods are not sufficiently promoted. Furthermore, there is no system in place to recognize users' positive emotions and provide additional rewards, making it difficult to encourage sustainable environmental protection activities. There is a need to solve this problem and link user behavior to environmental protection.

[1285] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes an input means used by the user for convenience, a data receiving means for receiving input data, a data analysis means for analyzing the received data, a point calculation means for calculating points based on the data analyzed by the data analysis means and the results of the emotion engine, a point awarding means for awarding the calculated points to the user, a notification means for notifying the user of the awarded points, a linking means for linking data to other social networking services, a storage means for saving and managing accumulated data, an emotion engine for analyzing the user's emotional state using emotion recognition technology, and an environmental selection confirmation means for confirming that the user has made an environmentally friendly choice. This makes it possible to adjust rewards according to the user's emotional state and promote behavior that contributes to environmental protection.

[1286] An "input means" is a means by which a user provides information to the system, and is an input device that uses a smartphone or tablet.

[1287] "Data receiving means" refers to a means by which the server receives data input by the user, and has the function of receiving data mainly via the Internet.

[1288] "Data Analysis Means" means means for analyzing received data and interpreting the content of text and images using generative AI techniques and image analysis techniques.

[1289] An "emotion engine" is a technology for analyzing a user's emotional state, recognizing the user's emotions based on text and images.

[1290] The "point calculation means" is a means for calculating points to be awarded to a user based on the data analyzed by the data analysis means and the results of the emotion engine.

[1291] The "point granting means" is a means for granting calculated points to a user, and has the function of reflecting the points in the user's account.

[1292] "Notification means" refers to a means for notifying users of the points they have been awarded, such as sending information via push notification on a smartphone or email.

[1293] "Linking means" refers to a means for linking data to other social networking services, and automatically posting user actions to other platforms.

[1294] "Storage means" refers to the means for storing and managing accumulated data, and is used to later use the data stored on the server for analysis and management.

[1295] An "environmental choice confirmation means" is a means for a user to confirm that they have made an environmentally friendly choice (such as choosing a reusable container or a zero-emission delivery method).

[1296] The present invention provides an application for inputting information about environmentally friendly choices made by a user, and a system for calculating and awarding points by analyzing the data obtained thereby. The system includes an input means used by the user for convenience, a data receiving means for receiving the input data, a data analysis means for analyzing the received data, an emotion engine for analyzing the user's emotional state using emotion recognition technology, a point calculation means for calculating points, a point awarding means for awarding the calculated points to the user, a notification means for notifying the user of the awarded points, a linking means for linking data to other social networking services, a storage means for saving and managing the data, and an environmental selection confirmation means.

[1297] Hardware and software used

[1298] 1. Smartphones and tablets: These are devices where users input information.

[1299] 2. Server: Receives data, analyzes, stores, and notifies.

[1300] 3. Generative AI model: The model used to analyze data.

[1301] 4. Emotion Engine: Technology for analyzing the user's emotional state.

[1302] 5. Image analysis software: Techniques for analyzing the content of images.

[1303] Data processing and calculation

[1304] 1. Input method: Users use their smartphone or tablet to input information about environmentally friendly choices, such as reusable containers or zero-emission delivery methods. For example, if they choose a reusable container, they input the information and associated text and images.

[1305] 2. Data reception method: The input data is sent to the server via the Internet. The server stores the received data for analysis.

[1306] 3. Data analysis means: The server analyzes the received data using generative AI models and image analysis software. For example, the text analysis module extracts keywords such as "reusable containers" and "zero-emission delivery," and the image analysis module checks the content of the attached photos.

[1307] 4. Emotion Engine: The server analyzes the user's emotional state based on the text and images. It determines whether the user is satisfied from the text and identifies the emotion by analyzing the user's facial expression from the image.

[1308] 5. Point calculation method: The server calculates points based on the analysis results and the emotion engine results. For example, if a user selects "reusable container," basic points are awarded, and if a positive emotion is recognized, additional points are awarded.

[1309] 6. Point allocation means: The calculated points are allocated to the user's account by the server.

[1310] 7. Notification method: The server notifies the user of the points awarded. A push notification or email message stating "10 points awarded" is sent.

[1311] 8. Collaboration: The server can be configured to share user posts with other social networking services. For example, a message saying "I used a reusable container today" can be automatically posted to Twitter or Facebook.

[1312] 9. Storage: All received data, analysis results, emotion recognition results, and point allocation results will be stored on the server and used for long-term analysis and management.

[1313] 10. Environmental Choice Verification: The server analyzes information about reusable containers and zero-emission delivery methods to verify that the user has made an environmentally friendly choice.

[1314] Specific examples

[1315] For example, if a user selects a reusable container using a smartphone app and enters the text "I was very satisfied," this information is sent to a server via the Internet. The server then analyzes the text using a generative AI model to extract the keyword "reusable container." The emotion engine then recognizes positive emotions from the text "I was very satisfied." Based on the analysis results, basic points are awarded, and additional points are awarded because positive emotions were recognized. The final calculated points are reflected in the user's account, and a push notification is sent stating, "15 points awarded." If the user has configured this, the post is also automatically shared on social networking services.

[1316] Prompt Sentence Examples

[1317] "Analyze the sentiment of user reviews and rate the degree to which they express positive emotions. Sentence: Today's delivery was fantastic!"

[1318] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1319] Step 1:

[1320] Users input information into the application using a smartphone or tablet. Specifically, users select a reusable container or a zero-emission delivery method, and enter text such as "I was very satisfied." The input data includes text and, if necessary, images. The input data is sent to the endpoint via the Internet. The input data includes "order ID," "eco-selection," "delivery method," "user review," and "user image."

[1321] Step 2:

[1322] The server receives data sent from the input means using the data receiving means. The received data is stored in a database and is then analyzed. The input here is text and image data sent by the user, and the output is in the saved data format.

[1323] Step 3:

[1324] The server begins analyzing the received data using data analysis means. Specifically, it uses a generative AI model to analyze the text data and extract keywords related to eco-friendly choices. For example, keywords such as "reusable containers" and "zero-emission delivery" are extracted. The image data is analyzed using image analysis software to verify its content. The input is the stored data, and the output is the analysis results.

[1325] Step 4:

[1326] The emotion engine analyzes the emotional state of a user from text and images. In text analysis, positive emotions are recognized from expressions such as "I am very satisfied." In image analysis, emotions are identified from the user's facial expressions. The input is text and image data, and the output is an emotion score.

[1327] Step 5:

[1328] The server calculates points using a point calculation means based on the results of the data analysis means and the emotion engine. Base points are awarded according to the eco-friendly choices made by the user. In addition, additional points are calculated based on the emotion score. For example, selecting "reusable containers" earns 10 points, and positive emotions earn an additional 5 points. The inputs are the analysis results and the emotion score, and the output is the calculated points.

[1329] Step 6:

[1330] Using the point awarding means, the calculated points are awarded to the user's account. The server stores this information in a database. The input is the calculated points, and the output is the points reflected in the user's account.

[1331] Step 7:

[1332] Using the notification method, the server notifies the user of the points awarded. It sends a push notification or email notification with a message saying "15 points awarded." The input is the points reflected in the user's account, and the output is the notification message sent.

[1333] Step 8:

[1334] By using a linking mechanism, if the user selects it, the server automatically posts the data to other social networking services. For example, a message such as "I used a reusable container today" is posted to Twitter or Facebook. The input is the user's selected data, and the output is a post to another social networking service.

[1335] Step 9:

[1336] The server stores and manages all received data, analysis results, emotion recognition results, and point allocation results. The stored data is used for long-term analysis and management. The input is all analysis result data, and the output is the stored data.

[1337] Step 10:

[1338] Using the environmental choice confirmation method, the server confirms that the user has made an environmentally friendly choice (e.g., choosing a reusable container or a zero-emission delivery method). The server then verifies the environmentally friendly choice by comparing it with the analysis results. The input is the user's selection data, and the output is the confirmation result.

[1339] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[1340] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1341] 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 the present disclosure is not limited to this, and the specific processing may be performed by the headset type terminal 314.

[1342] [Fourth embodiment]

[1343] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1344] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[1345] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[1346] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[1347] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[1348] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[1349] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[1350] The control object 443 includes a display device, LEDs in the eyes, and motors for driving the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[1351] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[1352] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific 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.

[1353] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[1354] In the robot 414, the processor 46 performs the reception output process. A reception output program 60 is stored in the storage 50. 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 process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[1355] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1356] This invention is a system that awards points to users by entering "good deeds for the Earth" into a social networking app. This system provides continuous motivation for users to continue environmental protection activities easily, share their actions, and receive rewards.

[1357] System configuration

[1358] 1. Input Method

[1359] Users use social media apps to input text and images. For example, they might write, "I commuted by bicycle today," and attach a photo of their bicycle. This input method is done on devices such as smartphones and tablets.

[1360] 2. Means of receiving data

[1361] The data transmitted from the terminal is received by the server, which receives the data via the Internet and stores it for analysis.

[1362] 3. Data Analysis Methods

[1363] The server analyzes the received text and images using AI generative technology and image analysis technology. For example, the text analysis module extracts the keyword "bicycle commuting," and the image analysis module verifies that the attached photo is of a bicycle.

[1364] 4. Point Calculation Method

[1365] The server calculates the contribution of an action based on the results of the data analysis means, for example, "commuting by bicycle" is evaluated as being worth 10 points to environmental protection.

[1366] 5. Points Awarding Method

[1367] The server will then calculate the points and credit them to the user's account, which the user can view within the app.

[1368] 6. Means of notification

[1369] The server notifies the terminal of the result of point allocation. For example, a message saying "10 points have been allocated" is displayed.

[1370] 7. Collaboration Methods

[1371] The server then shares the user's posts with other social networking services. For example, if the user has enabled it, it can automatically post to Twitter or Facebook, "I commuted by bicycle today."

[1372] 8. Preservation means

[1373] The server stores all receipt data, analysis results, and point allocation results. This data is used for system management and further analysis.

[1374] Specific examples

[1375] A user enters "I used my own bottle today" into a social media app and attaches a photo. When this information is entered using a smartphone, the data is sent to a server via the internet. The server analyzes the received data using generative AI and checks the keyword "I used my own bottle" and the content of the photo. If this action is evaluated as contributing 5 points to environmental protection, the server will add 5 points to the user's account and notify them of this.

[1376] At the same time, this post is automatically posted to other social media accounts (e.g., Twitter) selected by the user, so that their personal bottle use activities can be widely shared.Finally, the server stores all data and uses it for long-term analysis and management.

[1377] In this way, the system provides users with an incentive to take sustainable actions to protect the environment and a means to widely share the results.

[1378] The processing flow will be explained below.

[1379] Step 1:

[1380] A user launches a social media app and accesses the posting form.

[1381] Step 2:

[1382] The user enters the text "I commuted to work by bicycle today" into the posting form and attaches a photo of their bicycle.

[1383] Step 3:

[1384] The user presses the submit button to confirm the input.

[1385] Step 4:

[1386] The terminal packages the input text and image data and sends it to the server.

[1387] Step 5:

[1388] The server receives the data sent from the terminal.

[1389] Step 6:

[1390] The server passes the received data to a text analysis module, which analyzes the text.

[1391] Specific operation: The generative AI analyzes the text and extracts the keyword "bicycle commuting."

[1392] Step 7:

[1393] The server passes the image data to an image analysis module, which analyzes the image.

[1394] What it does: Uses image classification techniques to verify that the attached image is a photo of a bicycle.

[1395] Step 8:

[1396] The server scores the contribution of the behavior based on the results of text analysis and image analysis.

[1397] Specific action: The action of "commuting by bicycle" is given 10 points.

[1398] Step 9:

[1399] The server references the user's account database and adds the calculated 10 points to the user's account.

[1400] Step 10:

[1401] The server generates a message informing the user of the points being awarded.

[1402] Specific action: Create a notification that says "10 points awarded."

[1403] Step 11:

[1404] The server generates a notification message and sends it to the terminal.

[1405] Step 12:

[1406] Display notification messages received by the device to the user.

[1407] What happens: A notification will appear in the app saying "10 points awarded."

[1408] Step 13:

[1409] The server then links the posts to other social networking services based on the user's settings.

[1410] Specific operation: Converts the posted data into a specified format and sends it to Twitter or Facebook.

[1411] Step 14:

[1412] The server stores the receipt data, analysis results, and point allocation results in a database.

[1413] Step 15:

[1414] The server obtains advertising revenue data from the management system and secures the points funds.

[1415] Step 16:

[1416] The server manages a portion of the advertising revenue as a source of points redemption, ensuring the sustainability of the system.

[1417] Example 1

[1418] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1419] Currently, there are only a limited number of systems that promote environmental protection activities, and most of them have difficulty providing continuous motivation to users. Furthermore, there is a lack of systems that analyze the accuracy of input information, appropriately evaluate it, and provide rewards. Furthermore, these systems are rarely linked to other online platforms and are rarely widely recognized, which poses the challenge of lacking continuous motivation.

[1420] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[1421] In this invention, the server includes an input means used by users for operation, a data receiving means for receiving input information, a data analysis means for analyzing the received information, a point calculation means for calculating points based on the analysis results, a point awarding means for awarding the calculated points to users, a notification means for notifying the users of the awarded points, a linking means for linking the information to other online platforms, and a storage means for saving and managing the accumulated information. This allows users to easily record their environmental protection activities, widely share those activities, and receive continuous motivation.

[1422] "User" refers to the entity that performs operations or inputs, and includes individuals and organizations.

[1423] "Input means" refers to a device or method for a user to input information such as text and images.

[1424] "Data receiving means" refers to a means for receiving input information and transmitting it to a server.

[1425] "Data Analysis Tools" refers to techniques and devices for analyzing received text and images.

[1426] "Point calculation means" refers to the technology or device used to evaluate user behavior and calculate points based on the results of data analysis.

[1427] "Points Granting Means" refers to the means by which calculated points are added to a User's account.

[1428] "Notification means" refers to the technology or device that notifies users of the results of point allocation.

[1429] "Linkage means" refers to the technology or device that allows users to share their input information and activities with other online platforms.

[1430] "Storage means" refers to the technology and devices used to store and manage received information, analysis results, and point allocation results.

[1431] "Artificial intelligence technology" refers to the technology of analyzing input text using techniques such as machine learning, deep learning, and natural language processing.

[1432] "Image analysis technology" refers to technology for analyzing input images and recognizing their contents.

[1433] "Online platform" refers to services and websites provided via the Internet, including social networking sites and information sharing services.

[1434] This invention is a system that awards points to users by entering "good deeds for the Earth" into a social networking app. This system provides continuous motivation for users to continue environmental protection activities easily, share their actions, and receive rewards.

[1435] First, users access a social networking app using a device such as a smartphone or tablet. The application provides an input method for users to enter their environmental conservation activities using text and images. For example, a user might enter, "I commuted to work by bicycle today," and attach a photo of their bicycle.

[1436] The data entered by the user through the input means is sent to the server via the data receiving means. The data sent from the terminal arrives at the server via the Internet and is temporarily stored in a database.

[1437] The server then uses data analysis tools to analyze the received text and images. Specifically, it combines generative AI technology (artificial intelligence technology) and image analysis technology to perform text analysis and image recognition. For example, the text analysis module extracts the keyword "bicycle commuting," and the image analysis module recognizes bicycles from the attached photo.

[1438] After the analysis is completed, the server uses a point calculation means to evaluate the contribution of the behavior based on the analysis results and calculate points. For example, "commuting by bicycle" is evaluated as being worth 10 points for environmental protection. This evaluation standard is preset in the system.

[1439] Once the points are calculated, the server activates a points granting means to grant the points to the user's account. The calculated points are reflected in the user's account information. The user can check the current number of points through the application.

[1440] The result of point allocation will be notified to the user through a notification method. Specifically, a message saying "10 points have been allocated" will be sent to the user's smartphone in real time.

[1441] Furthermore, through the integration method, users' posts about environmental protection activities can be automatically shared on other social networking sites (online platforms). For example, if the user has set it up in advance, the post can also be linked and shared on Twitter, Facebook, etc. This allows the user's activities to be widely recognized.

[1442] Finally, the server uses storage means to store the receipt data, analysis results, point allocation results, etc. for a long period of time. This data is used for system management and further analysis.

[1443] Specific examples

[1444] A user types "I used my own bottle today" into a social media app and attaches a photo. This data is sent from the device to a server via the internet. The server uses generative AI technology to analyze the received data and checks the keyword "I used my own bottle" and the content of the photo. This action is assessed as contributing 5 points to environmental protection, and 5 points are awarded to the user's account. The user is also notified of the result, and depending on their settings, it is automatically posted to other social media platforms such as Twitter. Finally, the server stores all data and uses it for long-term analysis and management.

[1445] Prompt Sentence Examples

[1446] "When a user enters their environmental protection actions into a social media app, the server analyzes the text and images using generative AI technology, calculates and awards points, and notifies the user of the results, and shares the post on other social media platforms. Please explain with a concrete example."

[1447] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1448] Step 1:

[1449] A user launches a social networking app and inputs text and images. For example, they might type, "I commuted by bicycle today," and attach a photo of their bicycle. Input is done using a device such as a smartphone or tablet.

[1450] Input: Text and images entered by the user.

[1451] Output: Data packets that the device sends to the server.

[1452] Specific behavior: A user enters text into an app's input field and attaches an image.

[1453] Step 2:

[1454] The terminal transmits the input data to a server via the Internet.

[1455] Input: Text and images entered by the user.

[1456] Output: The data sent to the server.

[1457] Specific operation: The terminal packetizes the data and sends an HTTP request. The data is encrypted and sent.

[1458] Step 3:

[1459] The server temporarily stores the data received from the terminal in a database.

[1460] Input: Data sent from the terminal.

[1461] Output: Data stored in the database.

[1462] Specific operation: The server parses the received JSON format data and issues an INSERT statement to the database.

[1463] Step 4:

[1464] The server analyzes the received text and images using generative AI technology and image analysis technology.

[1465] Input: Text and image data stored in a database.

[1466] Output: Parsed keywords and verification results.

[1467] How it works: The generative AI model analyzes the text and extracts the keyword "bicycle commuting." Image analysis technology recognizes bicycles from images.

[1468] Step 5:

[1469] The server calculates the contribution of the behavior based on the results of the data analysis, and calculates points.

[1470] Input: Parsed keywords and verification results.

[1471] Output: The calculated points.

[1472] What happens: The server calculates points using predefined rating logic. "Commuting by bicycle" is rated as 10 points.

[1473] Step 6:

[1474] The server credits the calculated points to the user's account.

[1475] Input: The calculated point.

[1476] Output: Points added to the user's account information.

[1477] Specific operation: The server executes an SQL query to update the user's account information and add new points to the current points.

[1478] Step 7:

[1479] The server notifies the terminal of the result of the point allocation.

[1480] Input: Point award results.

[1481] Output: Notification message to user terminal.

[1482] Specific operation: The server sends a push notification using a real-time notification service (e.g., Firebase Cloud Messaging).

[1483] Step 8:

[1484] The server then links the user's posts to other online platforms.

[1485] Input: User posts and collaboration settings.

[1486] Output: Posting to other online platforms.

[1487] Specific operation: The server uses OAuth to access the user's social media account and sends an API request to automatically post the configured content to Twitter or Facebook, for example.

[1488] Step 9:

[1489] The server stores and manages the receipt data, analysis results, and point allocation results over the long term.

[1490] Input: Received data, analysis results, point allocation results.

[1491] Output: Saved data.

[1492] Specific operation: The server issues an INSERT statement to the database to permanently store various data. The stored data can be used for future analysis and system audits.

[1493] Through these steps, users can record their environmental protection actions, receive points for their actions, and share them more widely, providing lasting motivation.

[1494] (Application example 1)

[1495] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1496] There is a need for a system that can sustainably encourage the efforts of individuals who engage in environmental protection activities and effectively share their actions. Conventional systems simply aggregate the environmental protection activities performed by users, without providing specific rewards or widespread sharing, making it difficult to maintain individual motivation. Furthermore, there is a lack of means to objectively evaluate the effectiveness of environmental protection activities. To solve these issues, a system is needed that automatically analyzes users' reports and provides motivation for continuous environmental protection activities through points awarding and sharing on social media.

[1497] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[1498] In this invention, the server includes an input means used by users for convenience, a data receiving means for receiving input data, a data analysis means for analyzing the received data, a point calculation means for calculating points based on the analysis results, a point awarding means for awarding the calculated points to users, a notification means for notifying the users of the awarded points, a linking means for linking data to other social networking services, a storage means for saving and managing accumulated data, a generation AI technology means for using generation AI technology as data analysis means, an image analysis means for using image analysis technology as image analysis means, and an SNS sharing means for sharing posts on SNS based on the user's reports. This allows points to be automatically awarded to users who engage in environmental conservation activities, and their actions can be widely shared, providing continuous motivation.

[1499] "Input means" refers to the means by which a user inputs information into the system, and includes devices such as smartphones and tablets.

[1500] The "data receiving means" is a means for receiving data entered by a user and transferring it to the server.

[1501] "Data analysis means" means means for analyzing received data and understanding and evaluating its content, including generative AI technology and image analysis technology.

[1502] The "point calculation means" is a means for evaluating the user's behavior and calculating points based on the results of the data analysis means.

[1503] The "point granting means" is a means for granting calculated points to a user's account.

[1504] The "notification means" is a means for notifying the user of the point allocation result.

[1505] "Linking means" refers to a means for linking data processed within the system with other social networking services.

[1506] "Storage means" refers to the means for storing and managing all data, including receipt data, analysis results, and point allocation results.

[1507] "Generative AI technology means" means means that use generative AI technology to analyze text or understand its content.

[1508] "Image analysis means" means means that uses image analysis techniques to analyze and evaluate the content of an image.

[1509] "SNS sharing means" refers to a means for automatically posting the content reported by a user to other social networking services.

[1510] This invention is a system that allows users to report their environmental protection activities, award points based on the content of the reports, and share the results on social media. This system is designed to continuously motivate users to participate in environmental protection activities, and is configured as follows:

[1511] System Overview

[1512] 1. User Input Method

[1513] Users can use their smartphones or smart glasses to input text and images about their environmental conservation activities. For example, they can write "I picked up trash in the city today" and attach a photo of themselves picking up trash.

[1514] 2. Means of receiving data

[1515] The text and image data sent from the device is sent over the Internet to a server, which receives and stores this data.

[1516] 3. Data Analysis Methods

[1517] The server analyzes the received data using generative AI (GPT-4) and image analysis technology (TensorFlow). The generative AI technology analyzes the input text and extracts keywords. The image analysis technology checks whether the attached image contains content related to environmental protection activities.

[1518] 4. Point Calculation Method

[1519] Based on the analysis results, the server calculates points corresponding to the user's actions. For example, "picking up trash" is valued at 20 points for environmental protection.

[1520] 5. Points Awarding Method

[1521] The calculated points are added to the user's account, and the user can check the points on their My Page within the app.

[1522] 6. Means of notification

[1523] The server notifies the user of the point allocation result. For example, a message saying "20 points have been allocated" is displayed in the app.

[1524] 7. Social Media Integration Methods

[1525] Depending on the user's settings, the report may also be automatically posted to other social networking services, such as Twitter, allowing users to share their environmental protection activities more widely.

[1526] 8. Preservation means

[1527] The server stores all receipt data, analysis results, and point allocation results, and this data is used for long-term analysis and system management.

[1528] Hardware and software used

[1529] Generative AI model: GPT-4 (used via Hugging Face, etc.)

[1530] Image analysis technology: TensorFlow

[1531] Server: AWS (Amazon Web Services)

[1532] Database: PostgreSQL

[1533] Social media APIs: Twitter API, etc.

[1534] Adding specific examples

[1535] For example, suppose a user uses their smartphone to type the text "I picked up trash on the street today" and attach a photo of themselves picking up trash. The server receives this data and analyzes it using generative AI technology (GPT-4) and image analysis technology (TensorFlow). If the analysis determines that "picking up trash" is worth 20 points, the server will add 20 points to the user's account and notify them of the result. At the same time, this report is automatically posted to Twitter. Finally, all data is stored in a database (PostgreSQL).

[1536] Prompt Sentence Examples

[1537] "Please generate an example program for a security service app that allows users to report environmental protection activities and award points based on those activities. The technologies used will be generative AI (GPT-4) and image analysis (TensorFlow). This application will be installed on smartphones and smart glasses."

[1538] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1539] Step 1:

[1540] Users use their smartphones or smart glasses to input text and images of environmental conservation activities.

[1541] Input: Text (e.g., "I picked up trash in the city today"), image (e.g., a photo of someone picking up trash).

[1542] Output: Input data is sent from the device to the server.

[1543] Step 2:

[1544] The server receives the text and image data sent from the terminal.

[1545] Input: Text and image data entered by the user.

[1546] Output: The server receives the data and stores it for further analysis steps.

[1547] Step 3:

[1548] The server uses the generative AI (GPT-4) to analyze the received text and extract keywords.

[1549] Input: Received text data.

[1550] Data processing: Use GPT-4 to process text using natural language processing and extract keywords.

[1551] Output: A list of keywords (e.g. "picking up trash").

[1552] Step 4:

[1553] The server uses image analysis technology (TensorFlow) to analyze the received image and confirm its contents.

[1554] Input: Received image data.

[1555] Data processing: Use TensorFlow to recognize and verify image content (e.g., litter collection activities).

[1556] Output: Image analysis results (e.g. matching score indicating litter picking).

[1557] Step 5:

[1558] The server calculates points based on the results of text analysis and image analysis.

[1559] Input: Text analysis results and image analysis results.

[1560] Data calculation: Points are calculated based on the type of environmental protection activity and its evaluation.

[1561] Output: Calculated points (e.g. 20 points).

[1562] Step 6:

[1563] The server will then credit the calculated points to the user's account.

[1564] Input: Calculated points, user account information.

[1565] Specific operation: Point update processing to the database.

[1566] Output: User account with points awarded.

[1567] Step 7:

[1568] The server notifies the user of the result of point allocation.

[1569] Input: Point allocation result, user device information.

[1570] What it does: Sends a notification message to the user's smartphone or smart glasses.

[1571] Output: A notification displayed on the user's device (e.g., "20 points awarded").

[1572] Step 8:

[1573] The server posts the user's report to the configured SNS in order to share it socially.

[1574] Input: User's report content (text and image), user's SNS connection settings.

[1575] Specific operation: Post via SNS API.

[1576] Output: Report posted on social media.

[1577] Step 9:

[1578] The server stores and manages all data (received data, analysis results, point allocation results).

[1579] Input: The output data for each step.

[1580] Specific operation: Saving to the database.

[1581] Output: The saved data is stored in a database.

[1582] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1583] This invention is a system that awards points to users by entering "good deeds for the Earth" into a social networking app. The system incorporates an emotion engine that recognizes the user's emotions, and adjusts the evaluation and rewards of actions according to the user's emotional state. This allows for detailed feedback to be given to users, strengthening their motivation to continue environmental protection activities.

[1584] System configuration

[1585] 1. Input Method

[1586] Users use social media apps to input text and images. For example, they can type "I picked up trash today" and attach a photo of the trash collection. This input method is done on devices such as smartphones and tablets.

[1587] 2. Means of receiving data

[1588] The data transmitted from the terminal is received by the server, which receives the data via the Internet and stores it for analysis.

[1589] 3. Data Analysis Methods

[1590] The server analyzes the received text and images using generative AI technology and image analysis technology. For example, the text analysis module extracts the keyword "litter picking," and the image analysis module verifies that the attached photo shows someone picking up trash.

[1591] 4. Emotion Engine

[1592] The server analyzes the received text and images using an emotion engine to determine the user's emotional state, for example, by determining the user's sense of satisfaction or accomplishment from the context of the text and analyzing the user's facial expression from the image.

[1593] 5. Point Calculation Method

[1594] The server scores the contribution of each behavior based on the results of the data analysis and emotion engine. Points are adjusted according to the user's emotional state. For example, additional points are awarded if positive emotions are expressed.

[1595] 6. Points Awarding Method

[1596] The server will then add the calculated points to the user's account, which the user can view within the app.

[1597] 7. Means of notification

[1598] The server notifies the terminal of the result of point allocation. For example, a message saying "10 points have been allocated" is displayed.

[1599] 8. Collaboration Methods

[1600] The server then shares the user's posts with other social networking services. For example, if the user has enabled it, it can automatically post to Twitter or Facebook, "I picked up trash today."

[1601] 9. Preservation means

[1602] The server stores all received data, analysis results, emotion recognition results, and point allocation results. This data is used for system management and further analysis.

[1603] Specific examples

[1604] A user enters "I used my own bottle today" into a social media app and attaches a photo of the bottle. When this information is entered using a smartphone, the data is sent to a server via the internet. The server analyzes the received data using a generative AI and checks the keyword "I used my own bottle" and the content of the photo. The emotion engine also determines whether the user's text expresses satisfaction. This action is assessed as a contribution of 5 points, and because the emotion is positive, an additional 2 points are awarded. A total of 7 points are awarded to the user's account, and they are notified.

[1605] At the same time, this post is automatically posted to other social media accounts (e.g., Twitter) selected by the user, so that their personal bottle use activities can be widely shared.Finally, the server stores all data and uses it for long-term analysis and management.

[1606] In this way, the system not only motivates users to take sustainable actions to protect the environment and provides a means to widely share their achievements, but also strengthens users' internal motivation through its emotional engine.

[1607] The processing flow will be explained below.

[1608] Step 1:

[1609] A user launches a social media app and accesses the posting form.

[1610] Step 2:

[1611] The user enters the text "I commuted to work by bicycle today" into the posting form and attaches a photo of their bicycle.

[1612] Step 3:

[1613] The user presses the submit button to confirm the input.

[1614] Step 4:

[1615] The terminal packages the input text and image data and sends it to the server.

[1616] Step 5:

[1617] The server receives the data sent from the terminal.

[1618] Step 6:

[1619] The server passes the received data to a text analysis module, which analyzes the text.

[1620] Specific operation: The generative AI analyzes the text and extracts the keyword "bicycle commuting."

[1621] Step 7:

[1622] The server passes the image data to an image analysis module, which analyzes the image.

[1623] What it does: Uses image classification techniques to verify that the attached image is a photo of a bicycle.

[1624] Step 8:

[1625] The server passes the text and image data to an emotion engine to analyze the user's emotional state.

[1626] Specific behavior: The emotion engine determines the user's satisfaction or sense of accomplishment from the context of the text, and identifies the user's emotional state by analyzing their facial expressions from images.

[1627] Step 9:

[1628] The server scores the contribution of the behavior based on the results of text analysis, image analysis, and sentiment analysis.

[1629] Specific behavior: The behavior of "commuting by bicycle" is given a score of 10 points, with an additional 2 points added because it expresses positive emotions.

[1630] Step 10:

[1631] The server references the user's account database and adds the calculated 12 points to the user's account.

[1632] Step 11:

[1633] The server generates a message informing the user of the points being awarded.

[1634] Specific Action: Create a notification that says "A total of 12 points have been awarded."

[1635] Step 12:

[1636] The server generates a notification message and sends it to the terminal.

[1637] Step 13:

[1638] Display notification messages received by the device to the user.

[1639] What happens: A notification will appear in the app saying "You have been awarded a total of 12 points."

[1640] Step 14:

[1641] The server then links the posts to other social networking services based on the user's settings.

[1642] Specific operation: Converts the posted data into a specified format and sends it to Twitter or Facebook.

[1643] Step 15:

[1644] The server stores the received data, analysis results, emotion recognition results, and point allocation results in a database.

[1645] Step 16:

[1646] The server obtains advertising revenue data from the management system and secures the points funds.

[1647] Step 17:

[1648] The server manages a portion of the advertising revenue as a source of points redemption, ensuring the sustainability of the system.

[1649] Example 2

[1650] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1651] While existing social networking apps and environmental protection activity apps have systems for rating users' behavior, they lack a mechanism for recognizing the user's emotional state and reflecting it in the rating. This results in a lack of internal motivation for users, leading to problems with users not continuing to participate for long. Furthermore, there are also insufficient ways to effectively share posts on other social networking services, limiting the effectiveness of publicizing environmental protection activities.

[1652] The specific processing by the specific processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes an input means used by the user for convenience, a data receiving means for receiving input data, a data analysis means for analyzing the received data, an emotion engine means for recognizing the user's emotional state, a point calculation means for calculating points based on the analysis results and the emotion engine results, a point awarding means for awarding the calculated points to the user, a notification means for notifying the user of the awarded points, a linking means for linking data to other social networking services, and a storage means for saving and managing the accumulated data. This enables detailed evaluation and feedback based on the user's emotions, thereby strengthening motivation for environmental protection activities. Furthermore, linking with other social networking services enables widespread sharing and public relations effects.

[1653] "Input means" refers to the device or method by which a user inputs data such as text or images via a social networking app.

[1654] "Data receiving means" refers to the function of a server or system that receives data entered by a user via the Internet.

[1655] "Data Analysis Means" means a system or method that analyzes received data to identify and evaluate user behavior and content.

[1656] "Emotion engine means" refers to a system or module that analyzes and identifies an emotional state from text or images entered by a user.

[1657] "Point calculation means" refers to a system or method that calculates points by evaluating the contribution of a user's actions and emotions based on the results of data analysis and the results of the emotion engine.

[1658] "Points Granting Means" refers to a system or method for granting calculated points to a user's account.

[1659] "Notification means" refers to a method or system for notifying users of the results of points awarded.

[1660] "Integration means" refers to a function that automatically links and shares user posts with other social networking services.

[1661] "Storage means" refers to a system or method for storing and managing data such as receipt data, analysis results, emotion recognition results, and point allocation results.

[1662] This invention is a system that awards points to users who perform environmental protection-related actions by entering the actions into a social networking app. The system incorporates an emotion engine that recognizes the user's emotional state, and the evaluation and rewards for the actions are adjusted according to the emotional state. The results are also shared in cooperation with other social networking services.

[1663] First, users use their smartphones, tablets, or other devices to enter "something good they've done for the Earth" into a social media app. This entry includes both text and images. For example, a user might post, "I picked up trash today," and attach a photo of the trash collection.

[1664] The device then sends the input data over the internet to a server. The server receives the data and analyzes the text and images using generative AI techniques (e.g., GPT-3) and image analysis techniques. For example, the text analysis module extracts the keyword "litter picking," and the image analysis module checks whether the attached photo shows someone picking up trash.

[1665] Additionally, the server uses an emotion engine to identify the user's emotional state, for example, by determining the user's satisfaction or accomplishment from the context of the text, or by analyzing the user's facial expressions from the image.

[1666] Next, the server scores the contribution of the action based on the results of the data analysis means and emotion engine. If a positive emotion is expressed, additional points are awarded. For example, the action of "picking up trash" is evaluated as having a contribution of 5 points, and an additional 2 points are awarded because the emotion is positive. In this way, a total of 7 points are awarded to the user.

[1667] The server will then add the points to the user's account, which the user can check in the app. The server will then notify the device of the points addition result, and the device will display a message to the user saying "7 points have been added."

[1668] In addition, user posts are automatically posted to other social media accounts (e.g., Twitter, Facebook) that have been set up. For example, if a user has set up Twitter integration, the content "I picked up trash today" will be automatically posted.

[1669] Finally, the server stores all received data, analysis results, emotion recognition results, and point allocation results, which are used for subsequent management and analysis.

[1670] For example, if a user types "I used my own bottle today" into a social media app and attaches a photo of their bottle, you might use the following prompt:

[1671] "When a user posts, 'I used my own bottle today,' and attaches a photo, build a system that analyzes the post, identifies keywords and emotions, and awards points."

[1672] "Please explain the system's processing procedure when a specific example of an action is posted: 'Picking up trash.'"

[1673] This system is highly effective because it not only motivates users to take sustainable actions to protect the environment and provides a means for them to widely share their achievements, but also strengthens users' internal motivation through its emotional engine.

[1674] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1675] Step 1:

[1676] Users can use a social networking app to enter text and images about the good things they have done for the Earth. For example, they can post, "I picked up trash today," and attach a photo of the trash collection.

[1677] Input: Text input and image file

[1678] Output: The text "I picked up trash today" is input as data and an image file is generated.

[1679] Step 2:

[1680] The terminal transmits the input data to a server via the Internet.

[1681] Input: User-entered text and image data

[1682] Output: Text and image data are sent to the server.

[1683] Step 3:

[1684] The server receives the data sent by the user.

[1685] Input: Text and image data sent over the internet

[1686] Output: Save the received data to the internal storage.

[1687] Step 4:

[1688] The server analyzes the text data using generative AI techniques, such as GPT-3, to extract keywords like "litter picking" from the text.

[1689] Input: Received text data

[1690] Output: Extracted keyword "litter picking"

[1691] Step 5:

[1692] The server analyzes the received image data using image analysis technology, specifically determining whether the image content shows litter picking.

[1693] Input: Received image data

[1694] Output: Image analysis results (e.g., "Image and confirmation of litter collection")

[1695] Step 6:

[1696] The server uses an emotion engine to identify the user's emotional state from the text and image data, specifically determining feelings of satisfaction and accomplishment.

[1697] Input: Parsed text and image data

[1698] Output: Sentiment analysis result (e.g. "Satisfied")

[1699] Step 7:

[1700] The server scores the contribution of the behavior based on the results of the data analysis means and the emotion engine, and gives additional points if positive emotions are expressed.

[1701] Input: Text analysis results, image analysis results, sentiment analysis results

[1702] Output: Points as score (e.g., 5 base points + 2 additional points for emotional state = 7 total points)

[1703] Step 8:

[1704] The server credits the calculated points to the user's account.

[1705] Input: Calculated points

[1706] Output: Points added to your account

[1707] Step 9:

[1708] The server notifies the terminal of the result of the point allocation.

[1709] Input: Point allocation result

[1710] Output: A notification message on the device saying "7 points awarded"

[1711] Step 10:

[1712] The terminal displays a notification message to the user.

[1713] Input: Notification message from the server

[1714] Output: The notification message displayed to the user

[1715] Step 11:

[1716] The server automatically posts the user's posts to other social networking services (e.g., Twitter, Facebook) that you have set up.

[1717] Input: User posted content and integration settings

[1718] Output: Posts displayed on other social networks

[1719] Step 12:

[1720] The server stores data such as receipt data, analysis results, emotion recognition results, and point allocation results. All data is stored in a database.

[1721] Input: Text data, image data, analysis results, emotional state, point allocation results

[1722] Output: A comprehensive saved dataset

[1723] (Application example 2)

[1724] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1725] Current food delivery services lack incentives for users to make environmentally friendly choices. This means that the use of reusable containers and the selection of zero-emission delivery methods are not sufficiently promoted. Furthermore, there is no system in place to recognize users' positive emotions and provide additional rewards, making it difficult to encourage sustainable environmental protection activities. There is a need to solve this problem and link user behavior to environmental protection.

[1726] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes an input means used by the user for convenience, a data receiving means for receiving input data, a data analysis means for analyzing the received data, a point calculation means for calculating points based on the data analyzed by the data analysis means and the results of the emotion engine, a point awarding means for awarding the calculated points to the user, a notification means for notifying the user of the awarded points, a linking means for linking data to other social networking services, a storage means for saving and managing accumulated data, an emotion engine for analyzing the user's emotional state using emotion recognition technology, and an environmental selection confirmation means for confirming that the user has made an environmentally friendly choice. This makes it possible to adjust rewards according to the user's emotional state and promote behavior that contributes to environmental protection.

[1727] An "input means" is a means by which a user provides information to the system, and is an input device that uses a smartphone or tablet.

[1728] "Data receiving means" refers to a means by which the server receives data input by the user, and has the function of receiving data mainly via the Internet.

[1729] "Data Analysis Means" means means for analyzing received data and interpreting the content of text and images using generative AI techniques and image analysis techniques.

[1730] An "emotion engine" is a technology for analyzing a user's emotional state, recognizing the user's emotions based on text and images.

[1731] The "point calculation means" is a means for calculating points to be awarded to a user based on the data analyzed by the data analysis means and the results of the emotion engine.

[1732] The "point granting means" is a means for granting calculated points to a user, and has the function of reflecting the points in the user's account.

[1733] "Notification means" refers to a means for notifying users of the points they have been awarded, such as sending information via push notification on a smartphone or email.

[1734] "Linking means" refers to a means for linking data to other social networking services, and automatically posting user actions to other platforms.

[1735] "Storage means" refers to the means for storing and managing accumulated data, and is used to later use the data stored on the server for analysis and management.

[1736] An "environmental choice confirmation means" is a means for a user to confirm that they have made an environmentally friendly choice (such as choosing a reusable container or a zero-emission delivery method).

[1737] The present invention provides an application for inputting information about environmentally friendly choices made by a user, and a system for calculating and awarding points by analyzing the data obtained thereby. The system includes an input means used by the user for convenience, a data receiving means for receiving the input data, a data analysis means for analyzing the received data, an emotion engine for analyzing the user's emotional state using emotion recognition technology, a point calculation means for calculating points, a point awarding means for awarding the calculated points to the user, a notification means for notifying the user of the awarded points, a linking means for linking data to other social networking services, a storage means for saving and managing the data, and an environmental selection confirmation means.

[1738] Hardware and software used

[1739] 1. Smartphones and tablets: These are devices where users input information.

[1740] 2. Server: Receives data, analyzes, stores, and notifies.

[1741] 3. Generative AI model: The model used to analyze data.

[1742] 4. Emotion Engine: Technology for analyzing the user's emotional state.

[1743] 5. Image analysis software: Techniques for analyzing the content of images.

[1744] Data processing and calculation

[1745] 1. Input method: Users use their smartphone or tablet to input information about environmentally friendly choices, such as reusable containers or zero-emission delivery methods. For example, if they choose a reusable container, they input the information and associated text and images.

[1746] 2. Data reception method: The input data is sent to the server via the Internet. The server stores the received data for analysis.

[1747] 3. Data analysis means: The server analyzes the received data using generative AI models and image analysis software. For example, the text analysis module extracts keywords such as "reusable containers" and "zero-emission delivery," and the image analysis module checks the content of the attached photos.

[1748] 4. Emotion Engine: The server analyzes the user's emotional state based on the text and images. It determines whether the user is satisfied from the text and identifies the emotion by analyzing the user's facial expression from the image.

[1749] 5. Point calculation method: The server calculates points based on the analysis results and the emotion engine results. For example, if a user selects "reusable container," basic points are awarded, and if a positive emotion is recognized, additional points are awarded.

[1750] 6. Point allocation means: The calculated points are allocated to the user's account by the server.

[1751] 7. Notification method: The server notifies the user of the points awarded. A push notification or email message stating "10 points awarded" is sent.

[1752] 8. Collaboration: The server can be configured to share user posts with other social networking services. For example, a message saying "I used a reusable container today" can be automatically posted to Twitter or Facebook.

[1753] 9. Storage: All received data, analysis results, emotion recognition results, and point allocation results will be stored on the server and used for long-term analysis and management.

[1754] 10. Environmental Choice Verification: The server analyzes information about reusable containers and zero-emission delivery methods to verify that the user has made an environmentally friendly choice.

[1755] Specific examples

[1756] For example, if a user selects a reusable container using a smartphone app and enters the text "I was very satisfied," this information is sent to a server via the Internet. The server then analyzes the text using a generative AI model to extract the keyword "reusable container." The emotion engine then recognizes positive emotions from the text "I was very satisfied." Based on the analysis results, basic points are awarded, and additional points are awarded because positive emotions were recognized. The final calculated points are reflected in the user's account, and a push notification is sent stating, "15 points awarded." If the user has configured this, the post is also automatically shared on social networking services.

[1757] Prompt Sentence Examples

[1758] "Analyze the sentiment of user reviews and rate the degree to which they express positive emotions. Sentence: Today's delivery was fantastic!"

[1759] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1760] Step 1:

[1761] Users input information into the application using a smartphone or tablet. Specifically, users select a reusable container or a zero-emission delivery method, and enter text such as "I was very satisfied." The input data includes text and, if necessary, images. The input data is sent to the endpoint via the Internet. The input data includes "order ID," "eco-selection," "delivery method," "user review," and "user image."

[1762] Step 2:

[1763] The server receives data sent from the input means using the data receiving means. The received data is stored in a database and is then analyzed. The input here is text and image data sent by the user, and the output is in the saved data format.

[1764] Step 3:

[1765] The server begins analyzing the received data using data analysis means. Specifically, it uses a generative AI model to analyze the text data and extract keywords related to eco-friendly choices. For example, keywords such as "reusable containers" and "zero-emission delivery" are extracted. The image data is analyzed using image analysis software to verify its content. The input is the stored data, and the output is the analysis results.

[1766] Step 4:

[1767] The emotion engine analyzes the emotional state of a user from text and images. In text analysis, positive emotions are recognized from expressions such as "I am very satisfied." In image analysis, emotions are identified from the user's facial expressions. The input is text and image data, and the output is an emotion score.

[1768] Step 5:

[1769] The server calculates points using a point calculation means based on the results of the data analysis means and the emotion engine. Base points are awarded according to the eco-friendly choices made by the user. In addition, additional points are calculated based on the emotion score. For example, selecting "reusable containers" earns 10 points, and positive emotions earn an additional 5 points. The inputs are the analysis results and the emotion score, and the output is the calculated points.

[1770] Step 6:

[1771] Using the point awarding means, the calculated points are awarded to the user's account. The server stores this information in a database. The input is the calculated points, and the output is the points reflected in the user's account.

[1772] Step 7:

[1773] Using the notification method, the server notifies the user of the points awarded. It sends a push notification or email notification with a message saying "15 points awarded." The input is the points reflected in the user's account, and the output is the notification message sent.

[1774] Step 8:

[1775] By using a linking mechanism, if the user selects it, the server automatically posts the data to other social networking services. For example, a message such as "I used a reusable container today" is posted to Twitter or Facebook. The input is the user's selected data, and the output is a post to another social networking service.

[1776] Step 9:

[1777] The server stores and manages all received data, analysis results, emotion recognition results, and point allocation results. The stored data is used for long-term analysis and management. The input is all analysis result data, and the output is the stored data.

[1778] Step 10:

[1779] Using the environmental choice confirmation method, the server confirms that the user has made an environmentally friendly choice (e.g., choosing a reusable container or a zero-emission delivery method). The server then verifies the environmentally friendly choice by comparing it with the analysis results. The input is the user's selection data, and the output is the confirmation result.

[1780] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[1781] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1782] 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 the present disclosure is not limited to this, and the specific processing may be performed by the robot 414.

[1783] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[1784] FIG. 9 is a diagram illustrating an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and actions arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion includes both affect and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[1785] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[1786] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[1787] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. Emotions can also be created for robots, automobiles, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on Voice Emotion Recognition and Emotional Brain Physiological Signal Analysis Systems, Tokushima University, Doctoral Dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[1788] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[1789] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[1790] The system according to the present disclosure has been described above mainly with respect to the functions of the data processing device 12, but the system according to the present disclosure is not necessarily implemented on a server. The system according to the present disclosure may be implemented as a general information processing system. The present disclosure may be implemented, for example, as a software program running on a personal computer or an application running on a smartphone, etc. The method according to the present disclosure may be provided to users in the form of SaaS (Software as a Service).

[1791] In the above embodiment, an example was given in which the specific processing is performed by one computer 22, but the technology of the present disclosure is not limited to this, and the specific processing may be distributed and performed by a plurality of computers including the computer 22. For example, the data generation model 58 may be provided in an external device of the data processing device 12, and data may be generated in the external device in accordance with input data.

[1792] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[1793] 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.

[1794] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[1795] The hardware resource for executing a specific process can be any of the following processors: An example of a processor is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. Another example of a processor is a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[1796] The hardware resource that executes the specific processing may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific processing may be a single processor.

[1797] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[1798] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[1799] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[1800] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[1801] The following is further disclosed regarding the above embodiment.

[1802] (Claim 1)

[1803] An input means used by the user for convenience;

[1804] data receiving means for receiving input data;

[1805] data analysis means for analyzing the received data;

[1806] a point calculation means for calculating points based on the analysis result;

[1807] a point awarding means for awarding the calculated points to the user;

[1808] a notification means for notifying the user of the awarded points;

[1809] a means for linking data to other social networking services;

[1810] A storage means for storing and managing the accumulated data;

[1811] A system including:

[1812] (Claim 2)

[1813] 10. The system of claim 1, wherein the received data includes text and images.

[1814] (Claim 3)

[1815] 10. The system of claim 1, wherein the data analysis means uses generative AI techniques and image analysis techniques.

[1816] "Example 1"

[1817] (Claim 1)

[1818] An input means used by the user for operation;

[1819] data receiving means for receiving input information;

[1820] data analysis means for analyzing the received information;

[1821] a point calculation means for calculating points based on the analysis result;

[1822] a point awarding means for awarding the calculated points to the user;

[1823] a notification means for notifying the user of the awarded points;

[1824] Linking means to link information to other online platforms;

[1825] A storage means for storing and managing the accumulated information;

[1826] A system including:

[1827] (Claim 2)

[1828] 10. The system of claim 1, wherein the received information includes text and images.

[1829] (Claim 3)

[1830] 10. The system of claim 1, wherein the data analysis means uses artificial intelligence and image analysis techniques.

[1831] "Application Example 1"

[1832] (Claim 1)

[1833] An input means used by the user for convenience;

[1834] data receiving means for receiving input data;

[1835] data analysis means for analyzing the received data;

[1836] a point calculation means for calculating points based on the analysis result;

[1837] a point awarding means for awarding the calculated points to the user;

[1838] a notification means for notifying the user of the awarded points;

[1839] a means for linking data to other social networking services;

[1840] A storage means for storing and managing the accumulated data;

[1841] a generative AI technology means for using generative AI technology as a data analysis means;

[1842] image analysis means that uses image analysis technology as the image analysis means;

[1843] A social media sharing method that shares posts to social media based on user reports;

[1844] A system including:

[1845] (Claim 2)

[1846] 10. The system of claim 1, wherein the received data includes text and images.

[1847] (Claim 3)

[1848] 10. The system of claim 1, wherein the data analysis means uses generative AI techniques and image analysis techniques.

[1849] "Example 2: Combining Emotion Engines"

[1850] (Claim 1)

[1851] An input means used by the user for convenience;

[1852] data receiving means for receiving input data;

[1853] data analysis means for analyzing the received data;

[1854] an emotion engine means for recognizing an emotional state of a user;

[1855] a point calculation means for calculating points based on the analysis result and the emotion engine result;

[1856] a point awarding means for awarding the calculated points to the user;

[1857] a notification means for notifying the user of the awarded points;

[1858] a means for linking data to other social networking services;

[1859] A storage means for storing and managing the accumulated data;

[1860] A system including:

[1861] (Claim 2)

[1862] 10. The system of claim 1, wherein the received data includes text and images.

[1863] (Claim 3)

[1864] 2. The system of claim 1, wherein the data analysis means uses generative AI techniques and image analysis techniques.

[1865] "Application example 2 when combining emotion engines"

[1866] (Claim 1)

[1867] An input means used by the user for convenience;

[1868] data receiving means for receiving input data;

[1869] data analysis means for analyzing the received data;

[1870] a point calculation means for calculating points based on the data analyzed by the data analysis means and the result of the emotion engine;

[1871] a point awarding means for awarding the calculated points to the user;

[1872] a notification means for notifying the user of the awarded points;

[1873] a means for linking data to other social networking services;

[1874] A storage means for storing and managing the accumulated data;

[1875] an emotion engine that uses emotion recognition technology to analyze the user's emotional state;

[1876] an environmental choice confirmation means for confirming that the user has made an environmentally friendly choice;

[1877] A system including:

[1878] (Claim 2)

[1879] 10. The system of claim 1, wherein the received data includes text and images.

[1880] (Claim 3)

[1881] 10. The system of claim 1, wherein the data analysis means uses generative AI techniques and image analysis techniques. [Explanation of symbols]

[1882] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot< / url:> < / url:> < / url:> < / url:>

Claims

1. An input means used by the user for convenience; data receiving means for receiving input data; data analysis means for analyzing the received data; a point calculation means for calculating points based on the analysis result; a point awarding means for awarding the calculated points to the user; a notification means for notifying the user of the awarded points; a means for linking data to other social networking services; A storage means for storing and managing the accumulated data; A system including:

2. 10. The system of claim 1, wherein the received data includes text and images.

3. 10. The system of claim 1, wherein the data analysis means uses generative AI techniques and image analysis techniques.

Images