System

Abstract

A system is provided.SOLUTION: A system comprising a data collection means for collecting data from a user terminal, a data filtering means for anonymizing the data, a data transmission means for transmitting the anonymized data to a server, a server reception means for receiving and storing the data, a data analysis means for analyzing and reflecting the data in a generated AI model, and a reward management means for providing a reward for the data provision to a user.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] Advances in information and communications technology are driving demand for new services and plans in the mobile communications industry to meet the diverse needs of users. However, existing communications plans lack effective coordination between the collection of big data and the provision of user benefits, resulting in insufficient benefits for both parties. Furthermore, while protecting personal information is crucial, proper filtering and anonymization are often not implemented in the collection and use of such information. Therefore, new mobile communications plans are needed that provide benefits to both users and communications carriers while ensuring the protection of personal information. [Means for solving the problem]

[0005] To solve the above problems, the present invention provides a system including a data collection means for collecting data from user terminals, a data filtering means for anonymizing the data, a data transmission means for transmitting the anonymized data to a server, a server reception means for receiving and storing the data, a data analysis means for analyzing the data and reflecting the data in a generative AI model, and a reward management means for providing users with rewards for providing the data. This system allows users to receive rewards such as discounts on monthly communication fees and additional communication data volume, while enabling communication carriers to efficiently and securely collect the necessary big data. Furthermore, the anonymization of data ensures the protection of personal information.

[0006] "User terminal" refers to a mobile device, such as a mobile phone or smartphone, used by a user to collect and transmit data.

[0007] "Data Collection Methods" refers to software and hardware functions that collect certain data from a user device (e.g., SMS, browsing history, etc.).

[0008] "Data filtering measures" refers to algorithms or processes used to process collected data and remove or mask personally identifiable information.

[0009] "Data transmission means" refers to functionality for compressing the filtered data and transmitting it to a server using an appropriate secure protocol.

[0010] "Server receiving means" refers to the server-side function for receiving, decompressing, and saving data sent from a user terminal.

[0011] "Data analysis means" refers to the processes and algorithms used to analyze data stored on the server and use it as training data for the generative AI model.

[0012] "Benefit management means" refers to a function for calculating and providing benefits such as discounts on communication charges and additional data volume to users in exchange for providing data.

[0013] A "generative AI model" refers to an artificial intelligence model that learns from large amounts of data to perform natural language processing and other tasks. [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] The present invention relates to a system for collecting data from user terminals, filtering, anonymizing, transmitting, receiving, analyzing, and managing benefits. Specific embodiments for carrying out the present invention will be described below.

[0036] 1. Overall system configuration

[0037] The system of the present invention includes a user terminal, a server, and an interface for users to check rewards for providing data. The user terminal includes a data collection means, a data filtering means, and a data transmission means. Meanwhile, the server includes a server reception means, a data analysis means, and a reward management means.

[0038] 2. Data Collection and Filtering

[0039] A user launches a specific application on their device and agrees to provide data. Once this consent setting is confirmed, the user's device periodically triggers data collection methods to collect data such as SMS and browsing history. The collected data is anonymized by removing or masking personally identifiable information using data filtering methods. This anonymized data is then converted into a specified format, such as JSON.

[0040] 3. Sending and Receiving Data

[0041] The anonymized data is compressed by the data transmission means and transmitted to the server using a secure communication protocol. The server receives the data via the server reception means, decompresses it, and stores it in a database.

[0042] 4. Data analysis and reflection in AI models

[0043] The data stored on the server is analyzed by a data analysis means. The analyzed data is used as a training dataset for a generative AI model (such as ChatGPT) and is used to improve the accuracy of the model.

[0044] 5. Managing User Benefits

[0045] Users who agree to provide data will receive benefits such as discounts on communication charges and additional data volume depending on the amount of data provided. These benefits are calculated and managed by the server's benefit management means and reflected in the user's communication plan. Users can check the details of the benefits they have been granted through a dedicated application.

[0046] Specific examples

[0047] For example, consider the case where User A agrees to provide data. User A's device collects SMS data every day at 2:00 AM and removes phone numbers and names as part of personal information filtering. This filtered data is then converted into JSON format, compressed, and sent to the server. The server receives and stores this data, and uses it as training data for the AI ​​model. At the end of the month, the server calculates the amount of data provided by User A and grants discounts on communication charges and additional data volume. User A can check these benefits on the application.

[0048] The above is a specific embodiment of the present invention. This embodiment allows carriers to efficiently collect big data while providing substantial benefits to users. This system also meets the requirements for personal information protection, realizing a mechanism that benefits both users and carriers.

[0049] The processing flow will be explained below.

[0050] Step 1:

[0051] The user launches a dedicated application on the device and selects the option to consent to the provision of data. Once consent is confirmed, the device saves this setting in local storage and sends a consent notification to the server.

[0052] Step 2:

[0053] The device triggers a background task at the set interval (e.g., 2:00 AM every day) to begin data collection. The device retrieves and collects new messages from the SMS application.

[0054] Step 3:

[0055] The device scans the content of collected messages for personally identifiable information like phone numbers and names, and uses regular expressions to remove or mask this information to anonymize the data.

[0056] Step 4:

[0057] The device converts the anonymized data into JSON format and temporarily stores it locally, where it is then appropriately prepared as a file by the data filtering method.

[0058] Step 5:

[0059] The terminal compresses the filtered data using a method such as gzip and transmits it to the server using a secure communication protocol (e.g., HTTPS). The data transmission means manages this communication.

[0060] Step 6:

[0061] The server receives the HTTP request, decompresses the sent compressed data, verifies the data format and content for errors, and stores it in a local database.

[0062] Step 7:

[0063] The server analyzes the stored data. Using the data analysis means, the text data is processed with a natural language processing algorithm and necessary metadata (collection date and time, device ID, etc.) is added.

[0064] Step 8:

[0065] The server adds the analysis results to the training dataset of the generative AI model, which is then used to improve the accuracy of the generative AI model and develop new functions.

[0066] Step 9:

[0067] The server calculates the benefits for data-providing users and reflects them in the users' communication plans using a benefits management tool, often on a monthly basis.

[0068] Step 10:

[0069] The user checks the benefits through a dedicated application. The user opens the benefit confirmation screen on the application and obtains current benefit information from the server.

[0070] Step 11:

[0071] The device displays the benefit information it has obtained from the server on the user interface, allowing the user to check the benefits, such as discounts on communication charges and additional data volume.

[0072] Example 1

[0073] 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."

[0074] In modern information and communication technology, anonymization and efficient use of data collected from users are important challenges. There is also a need to improve the quantity and quality of collected data by offering appropriate rewards for the data provided by users. However, a system that efficiently anonymizes, compresses, securely transmits collected data, and then analyzes and manages rewards has yet to be fully developed. The present invention aims to solve these challenges and bring benefits to both users and service providers.

[0075] 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.

[0076] In this invention, the server includes a data collection means, a data filtering means, a data transmission means, a server receiving means, a data analysis means, and a reward management means. This allows data collected from user terminals to be converted into JSON format, compressed, and securely transmitted. Furthermore, by providing a means for setting consent to data provision via the user interface, a mechanism is provided for users to clearly consent to the provision of their own data, realizing a system that can provide appropriate rewards to users while thoroughly protecting personal information.

[0077] "Data collection means" refers to the functions and mechanisms for collecting data from user terminals.

[0078] "Data filtering measures" are functions or algorithms used to remove or mask personally identifiable information from collected data.

[0079] "Data transmission means" refers to the functions and protocols for transmitting anonymized data to a server.

[0080] "Server receiving means" refers to the function or mechanism by which the server receives and stores data.

[0081] "Data analysis means" refers to functions and algorithms for analyzing received data, which can be reflected in the generative AI model.

[0082] The "reward management means" refers to a function or module for providing users with rewards for providing data.

[0083] "Format conversion and compression means" refers to functions and algorithms for converting collected data into a specified format (e.g., JSON format) and compressing it.

[0084] "Consent setting means" refers to a function or interface that allows a user to set consent to the provision of data.

[0085] The present invention relates to a system for collecting data from user terminals, filtering, anonymizing, transmitting, receiving, analyzing, and managing benefits. Specific embodiments for carrying out the present invention will be described below.

[0086] 1. Overall system configuration

[0087] The system includes a user terminal, a server, and a user interface for users to check the rewards for providing data. The user terminal is equipped with a data collection means, a data filtering means, and a data transmission means. The server side includes a server reception means, a data analysis means, and a reward management means.

[0088] 2. Examples of Data Collection and Filtering

[0089] The user launches a dedicated application and agrees to provide data. Once consent is confirmed, the user's device periodically triggers data collection methods to collect data such as SMS and browsing history. For example, if a user configures SMS data collection at 2:00 AM every day, the data will be collected at the specified time. The collected data is anonymized by deleting or masking personal information. This anonymized data is then converted into JSON format.

[0090] 3. Examples of sending and receiving data

[0091] The anonymized data is compressed by the data transmission means and transmitted to the server using a secure communication protocol (such as HTTPS). The server receives the data via the server reception means, decompresses it, and stores it in a database.

[0092] 4. Analyzing the data and reflecting it in the generative AI model

[0093] The data stored on the server is analyzed by a data analysis means. The analyzed data is used as a training dataset for a generative AI model (e.g., ChatGPT). This training data improves the accuracy of the AI ​​model.

[0094] 5. Examples of User Benefit Management

[0095] Users who agree to provide data will receive benefits such as discounts on communication charges and additional data volume depending on the amount of data provided. These benefits are calculated and managed by the server's benefit management means and reflected in the user's communication plan. Users can check the details of the benefits they have been granted through a dedicated application.

[0096] Prompt Sentence Examples

[0097] Possible input prompts for a generative AI model include:

[0098] "Using the SMS data provided by users, please analyze their usage patterns."

[0099] "Guess your interests based on your browsing history."

[0100] By integrating the above functions, this system efficiently collects data from users, analyzes it, and uses it to train AI models. Furthermore, by offering appropriate benefits to users in exchange for providing their data, it has created a system that benefits both users and service providers.

[0101] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0102] Step 1:

[0103] The user launches the dedicated application and agrees to provide data.

[0104] What happens: The user opens the application, agrees to the terms and conditions for providing data, and then checks a specific box, after which their consent settings are saved within the app.

[0105] Input: User consent settings.

[0106] Output: The consent settings are saved and the next step is triggered.

[0107] Step 2:

[0108] The terminal periodically collects data using the data collection means.

[0109] Specific behavior: After consent is confirmed, the device's background service will be automatically triggered at a specific time (e.g., 2:00 AM) to collect necessary data, such as SMS data and browsing history.

[0110] Input: User consent settings and current time.

[0111] Output: Collected data (e.g. SMS, browsing history).

[0112] Step 3:

[0113] It filters the data collected by the device and anonymizes personal information.

[0114] What it does: Collected data is filtered to remove or mask any personally identifiable information, such as phone numbers and names, thereby anonymizing the data.

[0115] Input: Collected data.

[0116] Output: Anonymized data.

[0117] Step 4:

[0118] The device converts the anonymized data into JSON format, compresses it, and sends it to the server.

[0119] Specific operation: After the anonymized data is converted into JSON format, the data is compressed using a format such as ZIP and sent to the server using a secure communication protocol (e.g., HTTPS).

[0120] Input: Anonymized data.

[0121] Output: Compressed JSON data is sent to the server.

[0122] Step 5:

[0123] The server receives the data, unpacks it, and stores it in a database.

[0124] What happens: The server's API endpoint receives the data, unpacks it, and stores it in the database.

[0125] Input: Compressed JSON data.

[0126] Output: Data stored in a database.

[0127] Step 6:

[0128] The server analyzes the data using a data analysis means and reflects it in the training dataset of the generative AI model.

[0129] What it does: The stored data is analyzed and used as a training dataset for generative AI models (e.g., ChatGPT), using statistical methods and machine learning algorithms.

[0130] Input: Data stored in a database.

[0131] Output: Parsed data and a training dataset for a generative AI model.

[0132] Step 7:

[0133] The server calculates the amount of data provided by the user and grants rewards.

[0134] Specific operation: The server's reward management module aggregates the amount of data provided by each user and generates rewards such as discounts on communication charges or additional data volume based on the results.

[0135] Input: Parsed data and user-provided data volume.

[0136] Output: Benefit information (e.g., discounts on communication charges, additional data volume).

[0137] Step 8:

[0138] The user checks for offers through the application.

[0139] What it does: Users can open the "Rewards Information" section of the app and view details of the rewards they have been granted.

[0140] Input: Benefit information.

[0141] Output: Benefit details for user to review.

[0142] (Application example 1)

[0143] 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."

[0144] There is a need to improve production line efficiency by efficiently collecting and analyzing operational information, maintenance history, and defect information from robots used in factories. However, to effectively collect and analyze this data, it is essential to protect personal information and anonymize the data. It is also important to provide appropriate benefits to factory operators. This is expected to increase the efficiency of robot operations and improve the accuracy of predictive maintenance and defect detection.

[0145] 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.

[0146] In this invention, the server includes a data collection means for collecting data from user terminals, a data filtering means for anonymizing the data, a data transmission means for transmitting the anonymized data to the server, a server reception means for receiving and storing the data, a data analysis means for analyzing the data and reflecting it in a generative AI model, a reward management means for providing users with rewards for providing the data, and a factory robot having the function of collecting, anonymizing, compressing, and transmitting machine operation data, defect information, maintenance history, etc. This enables improved operational efficiency of the factory robot and improved accuracy of predictive maintenance and defect detection.

[0147] - "User terminal" means a device that has the ability to collect data and transmit it to a server.

[0148] "Data collection means" is a function for collecting necessary information from the terminal.

[0149] "Data filtering measures" are algorithms that remove or anonymize certain information from collected data.

[0150] The "data transmission means" is a function for transmitting anonymized data to a server.

[0151] The "server receiving means" is a function for receiving and storing transmitted data.

[0152] "Data analysis means" is a function for analyzing received data and reflecting it in the AI ​​model.

[0153] The "reward management means" is a function for calculating rewards for providing data and providing them to users.

[0154] A "factory robot" is a mechanical device that automates work within a factory and has the function of collecting operational data, maintenance history, and defect information.

[0155] A "generative AI model" is an artificial intelligence model that is trained based on analyzed data.

[0156] A "prompt" is an instruction or training example to be input into a generative AI model.

[0157] MODE FOR CARRYING OUT THE INVENTION

[0158] The present invention relates to a system that improves the efficiency of production lines by collecting and analyzing operation information, maintenance history, and defect information of robots used in factories. This system includes a user terminal, factory robots, a server, and an interface for privilege management.

[0159] Overall system configuration

[0160] The system of the present invention comprises the following components:

[0161] 1. User terminal: has data collection means, data filtering means, and data transmission means.

[0162] 2. Factory robots: They have the ability to collect machine operation data, malfunction information, maintenance history, etc., and transmit this information in an anonymized form.

[0163] 3. Server: Includes server receiving means, data analysis means, and benefit management means.

[0164] 4. Interface: Includes a means for users to check the rewards for the data they provide.

[0165] Data Collection and Filtering

[0166] The user (factory operator) launches a specific application on their device and agrees to provide data. Once this consent is confirmed, the factory robots will periodically collect data such as operation data, malfunction information, and maintenance history. The collected data is anonymized using data filtering methods.

[0167] Sending and Receiving Data

[0168] The anonymized data is compressed by the data transmission means and sent to the server. A secure protocol (e.g., SSL / TLS) is used for communication. The server receives the data via the server reception means, decompresses it, and stores it in a database.

[0169] Analyzing data and reflecting it in AI models

[0170] The data stored on the server is analyzed by a data analysis tool. The analyzed data is used as training data for a generative AI model (such as ChatGPT) and is used to improve the accuracy of the model.

[0171] Managing User Benefits

[0172] Users who agree to provide data will receive benefits according to the amount of data provided. These benefits include discounts on communication charges and additional data volume. These benefits are calculated and managed by a benefit management system and reflected in the user's communication plan. Users can check the details of the benefits they have received through a dedicated application.

[0173] Specific examples

[0174] For example, consider the case where a factory operator agrees to provide data. Factory robots collect operational data at a specific time each day and remove any personally identifiable information (such as robot ID and factory name). This filtered data is then converted into JSON format, compressed, and sent to a server. The server receives and stores this data, and uses it as training data for AI models. At the end of the month, the server calculates the amount of data provided by the user and awards them rewards. The user can view these rewards on the application.

[0175] Hardware and software used

[0176] Hardware: Factory robots, sensor devices

[0177] Software: Python, zlib library, requests library

[0178] Prompt Sentence Examples

[0179] "Design an application that collects operational data from factory robots, anonymizes it, and transmits it to a central server using a secure communication protocol. The central server analyzes the data and uses it as training data for a generative AI model."

[0180] This invention enables factory operators to carry out efficient operation and maintenance based on robot operational data, improving the accuracy of AI models.

[0181] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0182] Step 1:

[0183] The user device confirms consent to provide data. Once this consent is confirmed, the user device starts the process to collect the data. The input is the user's confirmed consent, and the output is the trigger for the data collection process. The specific behavior is that the user launches the application and clicks the consent button.

[0184] Step 2:

[0185] Factory robots collect operational data, malfunction information, maintenance history, etc. The collected data is sent as raw data to a user terminal. The inputs include the factory robot's operational data, malfunction records, and maintenance history, and the output is sent to the user terminal. Specifically, the data is collected using a sensor device and sent to the user terminal.

[0186] Step 3:

[0187] The collected data is filtered and anonymized on the user's device. Personally identifiable information (such as robot ID and factory name) is removed or masked. The input is the raw data sent by the factory robots, and the output is anonymized data. Specifically, an algorithm is run to remove identifying information.

[0188] Step 4:

[0189] The anonymized data is converted to JSON format and then compressed. This process converts the data into a transportable format. The input is the anonymized data, and the output is the data that has been converted to JSON format and compressed. The specific operations include converting the data to JSON format and then compressing it.

[0190] Step 5:

[0191] The compressed data is sent to the server using a secure communication protocol (e.g. SSL / TLS). The input is the compressed data, and the output is the securely transmitted data. The specific operation is to perform a request to send the data using the HTTPS protocol.

[0192] Step 6:

[0193] The server receives the transmitted data, decompresses it, and stores it in a database, which also serves as a repository for other data. The input is the compressed data, and the output is the decompressed data stored in the database. Specific operations include decompressing the data and writing it to the database.

[0194] Step 7:

[0195] The server analyzes the stored data and applies it to the generative AI model. The analyzed data is used as training data for the AI ​​model. The input is the raw data stored in the database, and the output is a trained AI model. Specific operations include applying a data analysis algorithm and training the AI ​​model.

[0196] Step 8:

[0197] The server grants rewards to users (factory operators) based on the amount of data provided. These rewards include discounts on communication charges and additional data volume. The input is the analyzed amount of data provided, and the output is the rewards granted. Specifically, the reward management means calculates the amount of data provided and applies the rewards to the user account.

[0198] Through these steps, the invention can efficiently and safely collect and analyze operational data from factory robots and use it to train AI models. Furthermore, by offering appropriate rewards to users for providing data, user satisfaction can be increased.

[0199] 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.

[0200] The present invention relates to a system that collects data from user terminals, anonymizes, transmits, receives, and analyzes the data, and further combines it with an emotion engine that recognizes the user's emotions. Specific embodiments for carrying out the present invention will be described below.

[0201] 1. Overall system configuration

[0202] The system of the present invention includes a user terminal, a server, and an interface for users to check rewards for providing data. The user terminal includes a data collection means, a data filtering means, a data transmission means, and an emotion engine. Meanwhile, the server includes a server reception means, a data analysis means, and a reward management means.

[0203] 2. Data Collection and Filtering

[0204] A user launches a specific application on their device and agrees to provide data. Once consent is confirmed, the user's device periodically triggers a data collection mechanism to collect data such as SMS and browsing history. The collected data is then anonymized by removing any personally identifiable information through a data filtering mechanism. This anonymized data is then converted into JSON format.

[0205] 3. Operation of the Emotion Engine

[0206] The anonymized data is then analyzed and labeled by a sentiment engine, which uses natural language processing algorithms to identify emotions within the text data and assigns corresponding labels (e.g., happy, sad, anger, etc.). This sentiment-labeled data is also stored in JSON format.

[0207] 4. Sending and Receiving Data

[0208] The anonymized data and emotion-labeled data are compressed by the data transmission means and transmitted to the server using a secure communication protocol. The server receives the data via the server reception means, decompresses it, and stores it in a database.

[0209] 5. Data analysis and reflection in AI models

[0210] The data stored on the server is analyzed by a data analysis tool. The analyzed data is used as a training dataset for a generative AI model (e.g., ChatGPT). Emotion-labeled data is also used as a training dataset, contributing to improving the accuracy of the AI ​​model.

[0211] 6. Managing User Benefits

[0212] Users who agree to provide data will receive benefits such as discounts on communication charges or additional data depending on the amount of data they provide. The benefit management method can also customize benefits based on emotion labels. For example, additional benefits can be provided to users with a high number of joy emotion labels.

[0213] 7. Check your benefits

[0214] Users can check their benefits through a dedicated application. When a user opens the benefit confirmation screen within the application, current benefit information is retrieved from the server and displayed.

[0215] Specific examples

[0216] For example, consider the case where User B agrees to provide data. User B's device collects SMS data every day at 2:00 AM and removes phone numbers and names as part of personal information filtering. This filtered data is then converted into JSON format, and an emotion engine assigns emotion labels to it. The emotion-labeled data is compressed and sent to the server, which receives and stores it. The server analyzes the received data and uses it as training data for the AI ​​model. At the end of the month, the server calculates discounts on communication charges and additional data volume based on User B's data provision volume and emotion labels, and grants benefits. User B can view these benefits on the application.

[0217] The above is a specific embodiment of the present invention, which allows carriers to efficiently collect big data while providing substantial benefits to users. The system satisfies the requirements for personal information protection and also improves the user experience by providing rewards that take user sentiment into account.

[0218] The processing flow will be explained below.

[0219] Step 1:

[0220] The user launches a dedicated application on the device and selects the option to consent to the provision of data. Once consent is confirmed, the device saves this setting in local storage and sends a consent notification to the server.

[0221] Step 2:

[0222] The device triggers a background task at a set interval (e.g., every day at 2:00 AM) to begin data collection. The device retrieves new data from SMS applications and browsing history.

[0223] Step 3:

[0224] The data collected by the device is processed using data filtering methods to detect personally identifiable information such as phone numbers, names, etc. This information is then removed or masked using regular expressions to anonymize the data.

[0225] Step 4:

[0226] The device converts the anonymized data into JSON format and temporarily stores it locally.

[0227] Step 5:

[0228] The device then runs an emotion engine that analyzes the anonymized data, using natural language processing algorithms to identify emotions within the text data and assigns them emotion labels such as "joy," "sadness," or "anger."

[0229] Step 6:

[0230] The emotion-labeled data is also stored in JSON format and compressed by the data transmission method.

[0231] Step 7:

[0232] The device sends the compressed data to the server using a secure communication protocol (e.g., HTTPS). An HTTP POST request is used for transmission.

[0233] Step 8:

[0234] The server receives the HTTP request, decompresses the compressed data, verifies the data format and content, and stores it in a local database.

[0235] Step 9:

[0236] The server analyzes the stored data using data analysis tools, and the analysis results are used as a training dataset for a generative AI model (e.g., ChatGPT).

[0237] Step 10:

[0238] The emotion-labeled data will also be analyzed and used as training data for the AI ​​model, which is expected to improve the accuracy of the AI ​​model's emotion recognition ability.

[0239] Step 11:

[0240] The server calculates rewards for data-providing users. Using reward management means, rewards are customized based on the amount of data provided by the user and their emotional labels. For example, users with a high number of "joy" emotional labels can be given additional rewards.

[0241] Step 12:

[0242] Users can check their benefits through a dedicated application. When a user opens the benefit confirmation screen within the application, current benefit information is retrieved from the server and displayed on the device screen.

[0243] Specific examples

[0244] For example, consider the case where User B agrees to provide data. User B's device collects SMS data every day at 2:00 AM, and personal information (phone number and name) is deleted using data filtering. The anonymized data is then converted into JSON format, and an emotion engine assigns an emotion label (e.g., "happy" or "sad"). This emotion-labeled data is compressed and sent to the server. The server receives the data, decompresses it, stores it, and analyzes it to use as training data for the AI ​​model. At the end of the month, the server calculates a discount on communication charges and additional data volume based on the amount of data provided by User B and the emotion label, and grants benefits. User B can view these benefits on the application.

[0245] In this way, the present invention realizes a series of processes: anonymizing, sentiment analysis, sending, receiving, and analyzing data collected from user devices, reflecting it in a generative AI model, and providing benefits to users. This provides a system that benefits both users and telecommunications carriers while thoroughly protecting personal information.

[0246] Example 2

[0247] 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."

[0248] In recent years, the spread of smart devices has led to a rapid increase in the amount of data generated by users. Systems that can effectively utilize this data and provide benefits to users are needed. However, an efficient system that consistently performs a series of processes, including data collection and anonymization, emotional labeling, secure data transmission, server analysis, and rewards management, has not yet been established. Furthermore, utilizing data while protecting user privacy is an important issue. Therefore, the present invention aims to provide a system that solves these problems.

[0249] The identification process by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes a data collection means for collecting data from user terminals, a data filtering means for anonymizing the data, a data transmission means for transmitting the anonymized data, a server reception means for receiving and storing the data, an emotion labeling means for assigning emotion labels to the anonymized data, a data analysis means for analyzing the labeled data and reflecting the results in a generative AI model, and a reward management means for providing users with rewards for providing the data. As a result, by consistently performing processes from data collection to analysis, emotion labeling, and reward management, it is possible to efficiently utilize user data and provide benefits to users.

[0250] "User terminal" means a device used by a user to generate and provide data, including a smartphone, tablet, or PC.

[0251] "Data Collection Measures" means functions or modules for collecting data generated by the User Device, which operate via specific applications or sensors.

[0252] "Data filtering measures" are functions or algorithms that remove or mask personally identifiable information from collected data.

[0253] "Data transmission means" refers to the functions and protocols for securely transmitting anonymized data to a server, and is often done via a communication line.

[0254] "Server receiving means" refers to a function or module for receiving data sent from a user terminal and saving it.

[0255] An "emotion labeling means" is a natural language processing algorithm or engine for assigning emotion labels to anonymized data.

[0256] "Data analysis means" refers to functions and modules that analyze received data and reflect it in the generative AI model.

[0257] The "reward management means" refers to a function or module for providing rewards to users in exchange for providing data, and is responsible for determining and notifying the content of the rewards.

[0258] A "generative AI model" is an artificial intelligence model that is trained using collected and analyzed data, and is used, for example, in natural language processing and sentiment analysis.

[0259] A "secure communication protocol" is a communication protocol that ensures security when sending and receiving data, and examples include HTTPS.

[0260] 1. Overall system configuration

[0261] The system of the present invention includes a user terminal, a server, and an interface for a user to check rewards for providing data. The user terminal includes a data collection means, a data filtering means, a data transmission means, and an emotion labeling means. Meanwhile, the server includes a server reception means, a data analysis means, and a reward management means.

[0262] 2. Data Collection and Filtering

[0263] Data collection is triggered when a user launches a specific application on their device and agrees to provide data. After confirming consent, the user's device periodically collects data such as SMS and browsing history. The collected data is anonymized by removing personally identifiable information using data filtering methods. This anonymized data is then converted to JSON format.

[0264] 3. Emotion Labeling

[0265] The anonymized data is then analyzed and labeled by a sentiment labeler, which uses natural language processing algorithms to identify emotions within the text data and assigns corresponding labels (e.g., happy, sad, anger, etc.). This sentiment-labeled data is also stored in JSON format.

[0266] 4. Data transmission

[0267] The anonymized data and emotion-labeled data are compressed by a data transmission means and transmitted to a server using a secure communication protocol. The device periodically transmits this data, allowing the server to store the latest data.

[0268] 5. Receiving and storing on the server

[0269] The server receives the data sent via the server receiving means, decompresses it, and stores it in a database. The server manages the received data and prepares it for analysis.

[0270] 6. Data analysis and reflection in AI models

[0271] The data analysis means on the server analyzes the stored data. The analyzed data is used as a training dataset for a generative AI model (e.g., ChatGPT). The emotion-labeled data is also used as a training dataset, contributing to improving the accuracy of the AI ​​model.

[0272] 7. Managing User Benefits

[0273] Users who agree to provide data can receive benefits based on the amount of data provided and the emotion label. The benefit management means manages these benefits and provides users with benefits such as discounts on communication charges or additional data.

[0274] 8. Check your benefits

[0275] Users can check their benefits through a dedicated application. When a user opens the benefit confirmation screen within the application, the latest benefit information is retrieved from the server and displayed.

[0276] Specific examples

[0277] For example, if a user agrees to provide data, the user's device will collect SMS data every day at 2:00 AM. Personally identifiable information (e.g., name, phone number) is removed from this data, anonymized, and converted into JSON format. An emotion labeling tool then assigns emotion labels such as "happy" or "sad." This labeled data is compressed and sent to a server via a secure communication protocol (e.g., HTTPS). The server receives the data, decompresses it, and stores it in a database. As a result of the analysis, new training data is added to the generative AI model, and the user can receive a discount on their communication charges at the end of the month, which can be confirmed in a dedicated app.

[0278] Prompt Sentence Examples

[0279] "SMS data collected from user devices is anonymized, emotion-labeled, converted to JSON format, and then sent to a server via a secure communication protocol. The received data is analyzed on the server and used as training data for the generative AI model. Rewards are then offered to users in the form of discounts on communication charges, which can then be viewed in the application."

[0280] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0281] Step 1: Start collecting data

[0282] The user launches the dedicated application on their device and agrees to provide data. This consent action initiates data collection. The input is the user's consent, and the output is the trigger for data collection. The specific action is when the user taps the "Data Provision Button."

[0283] Step 2: Collect data

[0284] The user device periodically collects data such as SMS and browsing history using a data collection method. The inputs are consent status and the specified data type, and the output is collected data. The specific operation is that the device scans and collects the target data at 2:00 AM every day.

[0285] Step 3: Filter and anonymize data

[0286] Personally identifiable information is removed from collected data using data filtering methods, resulting in anonymization. The input is collected raw data, and the output is anonymized data. Specifically, the process involves deleting the "Sato Taro" part from SMS data containing the name "Sato Taro" to anonymize it.

[0287] Step 4: Transforming the data and labeling it

[0288] The anonymized data is converted to JSON format, and then an emotion label is assigned to it by the emotion labeling means. The input is anonymized text data, and the output is JSON data with an emotion label assigned. The specific operation is to assign the label "joy" to the message "I'm so happy today!"

[0289] Step 5: Sending data

[0290] The emotion-labeled data is compressed and sent to the server by the data transmission means using a secure communication protocol. The input is emotion-labeled JSON data, and the output is a compressed data packet. The specific operation is to send the data to the server using HTTPS.

[0291] Step 6: Receiving and storing data

[0292] The server receives the data sent via the server receiving means, decompresses it, and stores it in the database. The input is the compressed data packet, and the output is the raw data stored in the database. The specific operation is to decompress the compressed data packet and insert it into the database.

[0293] Step 7: Analyze the data and incorporate it into the AI ​​model

[0294] The data analysis means in the server analyzes the stored data and reflects the results in the generative AI model. The input is the data to be analyzed, and the output is a training dataset for the generative AI model. The specific operation is to add emotion-labeled data to the dataset used for training.

[0295] Step 8: Manage and notify user benefits

[0296] The server calculates the rewards based on the amount of data provided by the user and the emotion label, and the reward management means notifies the user of the result. The input is the amount of data provided and the emotion label, and the output is the calculated reward information. The specific operation is to notify the user at the end of the month that "Your communication charges will be discounted by 20% depending on the amount of data provided."

[0297] Step 9: Check your benefits

[0298] The user checks the benefits through a dedicated application. The application retrieves the latest benefit information from the server and displays it. The input is a benefit confirmation request, and the output is the latest benefit information. The specific operation is for the user to select the "Benefits Confirmation" menu and check the current discount information and additional data volume.

[0299] (Application example 2)

[0300] 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."

[0301] Conventional advertising systems are unable to customize ads based on user emotions, making it difficult to deliver ads that effectively capture users' interests. Furthermore, from the perspective of protecting personal information, there was no system in place to safely manage collected data and provide users with special benefits.

[0302] The identification process by the identification 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 a data collection means for collecting data from user terminals, a data filtering means for anonymizing the data, and an emotion analysis means for assigning emotion labels to the anonymized data. This enables advertisement delivery based on user emotions.

[0303] A "user terminal" is a device such as a computer or mobile terminal used by a user.

[0304] A "data collection means" is a method or device for collecting data from a user terminal.

[0305] A "data filtering measure" is an algorithm or device used to remove or mask personally identifying information from collected data.

[0306] "Emotion analysis means" is a technology for identifying emotions in anonymized data and assigning emotion labels.

[0307] The "data transmission means" is a method or device for assigning emotion labels to anonymized data and transmitting it to a server.

[0308] The "server receiving means" is a method or device for receiving and storing data sent from a terminal.

[0309] "Data analysis means" refers to a method or device for analyzing received data and reflecting the results in the generative AI model.

[0310] The "reward management means" is a method or device for providing users with rewards for providing data and managing the rewards.

[0311] A "generative AI model" is an artificial intelligence model that is trained based on collected data.

[0312] "Emotion labels" are tags or marks that indicate a user's emotional state, obtained as a result of data analysis.

[0313] A "server" is a remote computing device that receives, analyzes, and stores data from multiple user terminals.

[0314] "Benefits" are rewards or benefits provided to users for actions such as providing data or viewing advertisements.

[0315] 1. Overall system configuration

[0316] This invention provides a system for realizing an emotion-intelligent advertising platform, including a user terminal, a server, and an interface for users to check rewards. The user terminal includes a data collection means, a data filtering means, an emotion analysis means, and a data transmission means. The server includes a server receiving means, a data analysis means, and a reward management means.

[0317] 2. Data Collection

[0318] When a user views an advertisement, the data collection means on the user's device uses the smartphone camera and built-in microphone to collect facial and voice data, which may reveal emotions such as interest, joy, indifference, and anger.

[0319] 3. Data Filtering

[0320] The collected data is anonymized using data filtering measures, such as face blurring, to remove any personally identifiable information, thereby protecting user privacy.

[0321] 4. Emotion analysis

[0322] The anonymized data is analyzed using natural language processing algorithms through sentiment analysis, for example using the Hugging Face transformers library to classify user emotions into labels such as "interested," "delighted," or "indifferent."

[0323] 5. Data Transmission

[0324] The emotion-labeled data is compressed by the data transmission means, encrypted using the HTTPS protocol, and sent to the server, ensuring data security.

[0325] 6. Data Reception and Analysis

[0326] The server receives the data sent via the server receiving means and stores it in a database. It then analyzes the data using the data analysis means as a training dataset for the generative AI model. In this process, it is possible to identify behavioral patterns and trends that are useful for ad delivery.

[0327] 7. Customizing and Offering Rewards

[0328] Users who consent to providing their data will be offered rewards using reward management tools. Rewards are customized based on emotional labels, so that, for example, users who are labeled as "interested" in an advertisement will receive additional rewards.

[0329] Specific examples

[0330] For example, let's say User A is viewing an advertisement on their smartphone. While viewing the advertisement, the smartphone's camera captures User A's facial expressions, and the built-in microphone collects their audio reactions. This data is blurred to anonymize personal information. Next, a natural language processing algorithm assigns an emotion label of "interest." The labeled data is securely sent to a server using HTTPS and analyzed there. The generated AI model uses this data to deliver new advertisements relevant to User A, thereby achieving more effective marketing. User A also earns points by viewing advertisements, which they can use to check for benefits such as discounts on communication charges within the app.

[0331] An example prompt might be something like, "Optimize the next ad shown to users based on the emotion label (e.g., interest, delight, indifference) they received when viewing a specific product ad."

[0332] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0333] Step 1:

[0334] The user device collects facial expression and voice data from the user while viewing the advertisement. Data is input using the camera and microphone of the smartphone or head-mounted display. This collected data becomes the basis for reflecting the user's emotions.

[0335] Step 2:

[0336] The collected data is then processed on the device using data filtering to remove personal information. Specifically, OpenCV is used to blur faces and anonymize the data. This process converts the raw data into anonymous data with personal information removed.

[0337] Step 3:

[0338] The anonymized data is then analyzed by a sentiment analysis tool, which uses Hugging Face's transformers library to run natural language processing algorithms. Based on the input anonymous data, the user's emotions are labeled as "interest," "happiness," "indifference," etc., generating emotion-labeled data. This data can then be used to deliver advertisements.

[0339] Step 4:

[0340] The emotion-labeled data is compressed by the data transmission means and sent to the server using the HTTPS protocol. The data is protected by a secure communication means and is therefore transported safely. In this step, the emotion-labeled data is transferred to the server.

[0341] Step 5:

[0342] The server receives the data sent from the terminal using the server receiving means. The received data is temporarily saved and stored in a database. The saved data is ready for analysis.

[0343] Step 6:

[0344] The data stored on the server undergoes detailed analysis by a data analysis tool. The analysis results are used as a training dataset for the generative AI model. The accuracy of the AI ​​model is improved based on the input emotion-labeled data. The results of the analysis are reflected in the ad distribution system.

[0345] Step 7:

[0346] The server uses a reward management means to provide rewards to users who agree to provide data. The rewards are customized based on the emotion label. For example, if a user is labeled as "interested," an additional reward is provided. The output data with the rewards is distributed to the user as a final notification, which the user can view in a dedicated application.

[0347] 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.

[0348] 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.

[0349] 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.

[0350] [Second embodiment]

[0351] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0352] 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.

[0353] 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).

[0354] 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.

[0355] 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.

[0356] 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).

[0357] 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.

[0358] 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.

[0359] 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.

[0360] 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.

[0361] 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.

[0362] 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."

[0363] The present invention relates to a system for collecting data from user terminals, filtering, anonymizing, transmitting, receiving, analyzing, and managing benefits. Specific embodiments for carrying out the present invention will be described below.

[0364] 1. Overall system configuration

[0365] The system of the present invention includes a user terminal, a server, and an interface for users to check rewards for providing data. The user terminal includes a data collection means, a data filtering means, and a data transmission means. Meanwhile, the server includes a server reception means, a data analysis means, and a reward management means.

[0366] 2. Data Collection and Filtering

[0367] A user launches a specific application on their device and agrees to provide data. Once this consent setting is confirmed, the user's device periodically triggers data collection methods to collect data such as SMS and browsing history. The collected data is anonymized by removing or masking personally identifiable information using data filtering methods. This anonymized data is then converted into a specified format, such as JSON.

[0368] 3. Sending and Receiving Data

[0369] The anonymized data is compressed by the data transmission means and transmitted to the server using a secure communication protocol. The server receives the data via the server reception means, decompresses it, and stores it in a database.

[0370] 4. Data analysis and reflection in AI models

[0371] The data stored on the server is analyzed by a data analysis means. The analyzed data is used as a training dataset for a generative AI model (such as ChatGPT) and is used to improve the accuracy of the model.

[0372] 5. Managing User Benefits

[0373] Users who agree to provide data will receive benefits such as discounts on communication charges and additional data volume depending on the amount of data provided. These benefits are calculated and managed by the server's benefit management means and reflected in the user's communication plan. Users can check the details of the benefits they have been granted through a dedicated application.

[0374] Specific examples

[0375] For example, consider the case where User A agrees to provide data. User A's device collects SMS data every day at 2:00 AM and removes phone numbers and names as part of personal information filtering. This filtered data is then converted into JSON format, compressed, and sent to the server. The server receives and stores this data, and uses it as training data for the AI ​​model. At the end of the month, the server calculates the amount of data provided by User A and grants discounts on communication charges and additional data volume. User A can check these benefits on the application.

[0376] The above is a specific embodiment of the present invention. This embodiment allows carriers to efficiently collect big data while providing substantial benefits to users. This system also meets the requirements for personal information protection, realizing a mechanism that benefits both users and carriers.

[0377] The processing flow will be explained below.

[0378] Step 1:

[0379] The user launches a dedicated application on the device and selects the option to consent to the provision of data. Once consent is confirmed, the device saves this setting in local storage and sends a consent notification to the server.

[0380] Step 2:

[0381] The device triggers a background task at the set interval (e.g., 2:00 AM every day) to begin data collection. The device retrieves and collects new messages from the SMS application.

[0382] Step 3:

[0383] The device scans the content of collected messages for personally identifiable information like phone numbers and names, and uses regular expressions to remove or mask this information to anonymize the data.

[0384] Step 4:

[0385] The device converts the anonymized data into JSON format and temporarily stores it locally, where it is then appropriately prepared as a file by the data filtering method.

[0386] Step 5:

[0387] The terminal compresses the filtered data using a method such as gzip and transmits it to the server using a secure communication protocol (e.g., HTTPS). The data transmission means manages this communication.

[0388] Step 6:

[0389] The server receives the HTTP request, decompresses the sent compressed data, verifies the data format and content for errors, and stores it in a local database.

[0390] Step 7:

[0391] The server analyzes the stored data. Using the data analysis means, the text data is processed with a natural language processing algorithm and necessary metadata (collection date and time, device ID, etc.) is added.

[0392] Step 8:

[0393] The server adds the analysis results to the training dataset of the generative AI model, which is then used to improve the accuracy of the generative AI model and develop new functions.

[0394] Step 9:

[0395] The server calculates the benefits for data-providing users and reflects them in the users' communication plans using a benefits management tool, often on a monthly basis.

[0396] Step 10:

[0397] The user checks the benefits through a dedicated application. The user opens the benefit confirmation screen on the application and obtains current benefit information from the server.

[0398] Step 11:

[0399] The device displays the benefit information it has obtained from the server on the user interface, allowing the user to check the benefits, such as discounts on communication charges and additional data volume.

[0400] Example 1

[0401] 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."

[0402] In modern information and communication technology, anonymization and efficient use of data collected from users are important challenges. There is also a need to improve the quantity and quality of collected data by offering appropriate rewards for the data provided by users. However, a system that efficiently anonymizes, compresses, securely transmits collected data, and then analyzes and manages rewards has yet to be fully developed. The present invention aims to solve these challenges and bring benefits to both users and service providers.

[0403] 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.

[0404] In this invention, the server includes a data collection means, a data filtering means, a data transmission means, a server receiving means, a data analysis means, and a reward management means. This allows data collected from user terminals to be converted into JSON format, compressed, and securely transmitted. Furthermore, by providing a means for setting consent to data provision via the user interface, a mechanism is provided for users to clearly consent to the provision of their own data, realizing a system that can provide appropriate rewards to users while thoroughly protecting personal information.

[0405] "Data collection means" refers to the functions and mechanisms for collecting data from user terminals.

[0406] "Data filtering measures" are functions or algorithms used to remove or mask personally identifiable information from collected data.

[0407] "Data transmission means" refers to the functions and protocols for transmitting anonymized data to a server.

[0408] "Server receiving means" refers to the function or mechanism by which the server receives and stores data.

[0409] "Data analysis means" refers to functions and algorithms for analyzing received data, which can be reflected in the generative AI model.

[0410] The "reward management means" refers to a function or module for providing users with rewards for providing data.

[0411] "Format conversion and compression means" refers to functions and algorithms for converting collected data into a specified format (e.g., JSON format) and compressing it.

[0412] "Consent setting means" refers to a function or interface that allows a user to set consent to the provision of data.

[0413] The present invention relates to a system for collecting data from user terminals, filtering, anonymizing, transmitting, receiving, analyzing, and managing benefits. Specific embodiments for carrying out the present invention will be described below.

[0414] 1. Overall system configuration

[0415] The system includes a user terminal, a server, and a user interface for users to check the rewards for providing data. The user terminal is equipped with a data collection means, a data filtering means, and a data transmission means. The server side includes a server reception means, a data analysis means, and a reward management means.

[0416] 2. Examples of Data Collection and Filtering

[0417] The user launches a dedicated application and agrees to provide data. Once consent is confirmed, the user's device periodically triggers data collection methods to collect data such as SMS and browsing history. For example, if a user configures SMS data collection at 2:00 AM every day, the data will be collected at the specified time. The collected data is anonymized by deleting or masking personal information. This anonymized data is then converted into JSON format.

[0418] 3. Examples of sending and receiving data

[0419] The anonymized data is compressed by the data transmission means and transmitted to the server using a secure communication protocol (such as HTTPS). The server receives the data via the server reception means, decompresses it, and stores it in a database.

[0420] 4. Analyzing the data and reflecting it in the generative AI model

[0421] The data stored on the server is analyzed by a data analysis means. The analyzed data is used as a training dataset for a generative AI model (e.g., ChatGPT). This training data improves the accuracy of the AI ​​model.

[0422] 5. Examples of User Benefit Management

[0423] Users who agree to provide data will receive benefits such as discounts on communication charges and additional data volume depending on the amount of data provided. These benefits are calculated and managed by the server's benefit management means and reflected in the user's communication plan. Users can check the details of the benefits they have been granted through a dedicated application.

[0424] Prompt Sentence Examples

[0425] Possible input prompts for a generative AI model include:

[0426] "Using the SMS data provided by users, please analyze their usage patterns."

[0427] "Guess your interests based on your browsing history."

[0428] By integrating the above functions, this system efficiently collects data from users, analyzes it, and uses it to train AI models. Furthermore, by offering appropriate benefits to users in exchange for providing their data, it has created a system that benefits both users and service providers.

[0429] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0430] Step 1:

[0431] The user launches the dedicated application and agrees to provide data.

[0432] What happens: The user opens the application, agrees to the terms and conditions for providing data, and then checks a specific box, after which their consent settings are saved within the app.

[0433] Input: User consent settings.

[0434] Output: The consent settings are saved and the next step is triggered.

[0435] Step 2:

[0436] The terminal periodically collects data using the data collection means.

[0437] Specific behavior: After consent is confirmed, the device's background service will be automatically triggered at a specific time (e.g., 2:00 AM) to collect necessary data, such as SMS data and browsing history.

[0438] Input: User consent settings and current time.

[0439] Output: Collected data (e.g. SMS, browsing history).

[0440] Step 3:

[0441] It filters the data collected by the device and anonymizes personal information.

[0442] What it does: Collected data is filtered to remove or mask any personally identifiable information, such as phone numbers and names, thereby anonymizing the data.

[0443] Input: Collected data.

[0444] Output: Anonymized data.

[0445] Step 4:

[0446] The device converts the anonymized data into JSON format, compresses it, and sends it to the server.

[0447] Specific operation: After the anonymized data is converted into JSON format, the data is compressed using a format such as ZIP and sent to the server using a secure communication protocol (e.g., HTTPS).

[0448] Input: Anonymized data.

[0449] Output: Compressed JSON data is sent to the server.

[0450] Step 5:

[0451] The server receives the data, unpacks it, and stores it in a database.

[0452] What happens: The server's API endpoint receives the data, unpacks it, and stores it in the database.

[0453] Input: Compressed JSON data.

[0454] Output: Data stored in a database.

[0455] Step 6:

[0456] The server analyzes the data using a data analysis means and reflects it in the training dataset of the generative AI model.

[0457] What it does: The stored data is analyzed and used as a training dataset for generative AI models (e.g., ChatGPT), using statistical methods and machine learning algorithms.

[0458] Input: Data stored in a database.

[0459] Output: Parsed data and a training dataset for a generative AI model.

[0460] Step 7:

[0461] The server calculates the amount of data provided by the user and grants rewards.

[0462] Specific operation: The server's reward management module aggregates the amount of data provided by each user and generates rewards such as discounts on communication charges or additional data volume based on the results.

[0463] Input: Parsed data and user-provided data volume.

[0464] Output: Benefit information (e.g., discounts on communication charges, additional data volume).

[0465] Step 8:

[0466] The user checks for offers through the application.

[0467] What it does: Users can open the "Rewards Information" section of the app and view details of the rewards they have been granted.

[0468] Input: Benefit information.

[0469] Output: Benefit details for user to review.

[0470] (Application example 1)

[0471] 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."

[0472] There is a need to improve production line efficiency by efficiently collecting and analyzing operational information, maintenance history, and defect information from robots used in factories. However, to effectively collect and analyze this data, it is essential to protect personal information and anonymize the data. It is also important to provide appropriate benefits to factory operators. This is expected to increase the efficiency of robot operations and improve the accuracy of predictive maintenance and defect detection.

[0473] 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.

[0474] In this invention, the server includes a data collection means for collecting data from user terminals, a data filtering means for anonymizing the data, a data transmission means for transmitting the anonymized data to the server, a server reception means for receiving and storing the data, a data analysis means for analyzing the data and reflecting it in a generative AI model, a reward management means for providing users with rewards for providing the data, and a factory robot having the function of collecting, anonymizing, compressing, and transmitting machine operation data, defect information, maintenance history, etc. This enables improved operational efficiency of the factory robot and improved accuracy of predictive maintenance and defect detection.

[0475] - "User terminal" means a device that has the ability to collect data and transmit it to a server.

[0476] "Data collection means" is a function for collecting necessary information from the terminal.

[0477] "Data filtering measures" are algorithms that remove or anonymize certain information from collected data.

[0478] The "data transmission means" is a function for transmitting anonymized data to a server.

[0479] The "server receiving means" is a function for receiving and storing transmitted data.

[0480] "Data analysis means" is a function for analyzing received data and reflecting it in the AI ​​model.

[0481] The "reward management means" is a function for calculating rewards for providing data and providing them to users.

[0482] A "factory robot" is a mechanical device that automates work within a factory and has the function of collecting operational data, maintenance history, and defect information.

[0483] A "generative AI model" is an artificial intelligence model that is trained based on analyzed data.

[0484] A "prompt" is an instruction or training example to be input into a generative AI model.

[0485] MODE FOR CARRYING OUT THE INVENTION

[0486] The present invention relates to a system that improves the efficiency of production lines by collecting and analyzing operation information, maintenance history, and defect information of robots used in factories. This system includes a user terminal, factory robots, a server, and an interface for privilege management.

[0487] Overall system configuration

[0488] The system of the present invention comprises the following components:

[0489] 1. User terminal: has data collection means, data filtering means, and data transmission means.

[0490] 2. Factory robots: They have the ability to collect machine operation data, malfunction information, maintenance history, etc., and transmit this information in an anonymized form.

[0491] 3. Server: Includes server receiving means, data analysis means, and benefit management means.

[0492] 4. Interface: Includes a means for users to check the rewards for the data they provide.

[0493] Data Collection and Filtering

[0494] The user (factory operator) launches a specific application on their device and agrees to provide data. Once this consent is confirmed, the factory robots will periodically collect data such as operation data, malfunction information, and maintenance history. The collected data is anonymized using data filtering methods.

[0495] Sending and Receiving Data

[0496] The anonymized data is compressed by the data transmission means and sent to the server. A secure protocol (e.g., SSL / TLS) is used for communication. The server receives the data via the server reception means, decompresses it, and stores it in a database.

[0497] Analyzing data and reflecting it in AI models

[0498] The data stored on the server is analyzed by a data analysis tool. The analyzed data is used as training data for a generative AI model (such as ChatGPT) and is used to improve the accuracy of the model.

[0499] Managing User Benefits

[0500] Users who agree to provide data will receive benefits according to the amount of data provided. These benefits include discounts on communication charges and additional data volume. These benefits are calculated and managed by a benefit management system and reflected in the user's communication plan. Users can check the details of the benefits they have received through a dedicated application.

[0501] Specific examples

[0502] For example, consider the case where a factory operator agrees to provide data. Factory robots collect operational data at a specific time each day and remove any personally identifiable information (such as robot ID and factory name). This filtered data is then converted into JSON format, compressed, and sent to a server. The server receives and stores this data, and uses it as training data for AI models. At the end of the month, the server calculates the amount of data provided by the user and awards them rewards. The user can view these rewards on the application.

[0503] Hardware and software used

[0504] Hardware: Factory robots, sensor devices

[0505] Software: Python, zlib library, requests library

[0506] Prompt Sentence Examples

[0507] "Design an application that collects operational data from factory robots, anonymizes it, and transmits it to a central server using a secure communication protocol. The central server analyzes the data and uses it as training data for a generative AI model."

[0508] This invention enables factory operators to carry out efficient operation and maintenance based on robot operational data, improving the accuracy of AI models.

[0509] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0510] Step 1:

[0511] The user device confirms consent to provide data. Once this consent is confirmed, the user device starts the process to collect the data. The input is the user's confirmed consent, and the output is the trigger for the data collection process. The specific behavior is that the user launches the application and clicks the consent button.

[0512] Step 2:

[0513] Factory robots collect operational data, malfunction information, maintenance history, etc. The collected data is sent as raw data to a user terminal. The inputs include the factory robot's operational data, malfunction records, and maintenance history, and the output is sent to the user terminal. Specifically, the data is collected using a sensor device and sent to the user terminal.

[0514] Step 3:

[0515] The collected data is filtered and anonymized on the user's device. Personally identifiable information (such as robot ID and factory name) is removed or masked. The input is the raw data sent by the factory robots, and the output is anonymized data. Specifically, an algorithm is run to remove identifying information.

[0516] Step 4:

[0517] The anonymized data is converted to JSON format and then compressed. This process converts the data into a transportable format. The input is the anonymized data, and the output is the data that has been converted to JSON format and compressed. The specific operations include converting the data to JSON format and then compressing it.

[0518] Step 5:

[0519] The compressed data is sent to the server using a secure communication protocol (e.g. SSL / TLS). The input is the compressed data, and the output is the securely transmitted data. The specific operation is to perform a request to send the data using the HTTPS protocol.

[0520] Step 6:

[0521] The server receives the transmitted data, decompresses it, and stores it in a database, which also serves as a repository for other data. The input is the compressed data, and the output is the decompressed data stored in the database. Specific operations include decompressing the data and writing it to the database.

[0522] Step 7:

[0523] The server analyzes the stored data and applies it to the generative AI model. The analyzed data is used as training data for the AI ​​model. The input is the raw data stored in the database, and the output is a trained AI model. Specific operations include applying a data analysis algorithm and training the AI ​​model.

[0524] Step 8:

[0525] The server grants rewards to users (factory operators) based on the amount of data provided. These rewards include discounts on communication charges and additional data volume. The input is the analyzed amount of data provided, and the output is the rewards granted. Specifically, the reward management means calculates the amount of data provided and applies the rewards to the user account.

[0526] Through these steps, the invention can efficiently and safely collect and analyze operational data from factory robots and use it to train AI models. Furthermore, by offering appropriate rewards to users for providing data, user satisfaction can be increased.

[0527] 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.

[0528] The present invention relates to a system that collects data from user terminals, anonymizes, transmits, receives, and analyzes the data, and further combines it with an emotion engine that recognizes the user's emotions. Specific embodiments for carrying out the present invention will be described below.

[0529] 1. Overall system configuration

[0530] The system of the present invention includes a user terminal, a server, and an interface for users to check rewards for providing data. The user terminal includes a data collection means, a data filtering means, a data transmission means, and an emotion engine. Meanwhile, the server includes a server reception means, a data analysis means, and a reward management means.

[0531] 2. Data Collection and Filtering

[0532] A user launches a specific application on their device and agrees to provide data. Once consent is confirmed, the user's device periodically triggers a data collection mechanism to collect data such as SMS and browsing history. The collected data is then anonymized by removing any personally identifiable information through a data filtering mechanism. This anonymized data is then converted into JSON format.

[0533] 3. Operation of the Emotion Engine

[0534] The anonymized data is then analyzed and labeled by a sentiment engine, which uses natural language processing algorithms to identify emotions within the text data and assigns corresponding labels (e.g., happy, sad, anger, etc.). This sentiment-labeled data is also stored in JSON format.

[0535] 4. Sending and Receiving Data

[0536] The anonymized data and emotion-labeled data are compressed by the data transmission means and transmitted to the server using a secure communication protocol. The server receives the data via the server reception means, decompresses it, and stores it in a database.

[0537] 5. Data analysis and reflection in AI models

[0538] The data stored on the server is analyzed by a data analysis tool. The analyzed data is used as a training dataset for a generative AI model (e.g., ChatGPT). Emotion-labeled data is also used as a training dataset, contributing to improving the accuracy of the AI ​​model.

[0539] 6. Managing User Benefits

[0540] Users who agree to provide data will receive benefits such as discounts on communication charges or additional data depending on the amount of data they provide. The benefit management method can also customize benefits based on emotion labels. For example, additional benefits can be provided to users with a high number of joy emotion labels.

[0541] 7. Check your benefits

[0542] Users can check their benefits through a dedicated application. When a user opens the benefit confirmation screen within the application, current benefit information is retrieved from the server and displayed.

[0543] Specific examples

[0544] For example, consider the case where User B agrees to provide data. User B's device collects SMS data every day at 2:00 AM and removes phone numbers and names as part of personal information filtering. This filtered data is then converted into JSON format, and an emotion engine assigns emotion labels to it. The emotion-labeled data is compressed and sent to the server, which receives and stores it. The server analyzes the received data and uses it as training data for the AI ​​model. At the end of the month, the server calculates discounts on communication charges and additional data volume based on User B's data provision volume and emotion labels, and grants benefits. User B can view these benefits on the application.

[0545] The above is a specific embodiment of the present invention, which allows carriers to efficiently collect big data while providing substantial benefits to users. The system satisfies the requirements for personal information protection and also improves the user experience by providing rewards that take user sentiment into account.

[0546] The processing flow will be explained below.

[0547] Step 1:

[0548] The user launches a dedicated application on the device and selects the option to consent to the provision of data. Once consent is confirmed, the device saves this setting in local storage and sends a consent notification to the server.

[0549] Step 2:

[0550] The device triggers a background task at a set interval (e.g., every day at 2:00 AM) to begin data collection. The device retrieves new data from SMS applications and browsing history.

[0551] Step 3:

[0552] The data collected by the device is processed using data filtering methods to detect personally identifiable information such as phone numbers, names, etc. This information is then removed or masked using regular expressions to anonymize the data.

[0553] Step 4:

[0554] The device converts the anonymized data into JSON format and temporarily stores it locally.

[0555] Step 5:

[0556] The device then runs an emotion engine that analyzes the anonymized data, using natural language processing algorithms to identify emotions within the text data and assigns them emotion labels such as "joy," "sadness," or "anger."

[0557] Step 6:

[0558] The emotion-labeled data is also stored in JSON format and compressed by the data transmission method.

[0559] Step 7:

[0560] The device sends the compressed data to the server using a secure communication protocol (e.g., HTTPS). An HTTP POST request is used for transmission.

[0561] Step 8:

[0562] The server receives the HTTP request, decompresses the compressed data, verifies the data format and content, and stores it in a local database.

[0563] Step 9:

[0564] The server analyzes the stored data using data analysis tools, and the analysis results are used as a training dataset for a generative AI model (e.g., ChatGPT).

[0565] Step 10:

[0566] The emotion-labeled data will also be analyzed and used as training data for the AI ​​model, which is expected to improve the accuracy of the AI ​​model's emotion recognition ability.

[0567] Step 11:

[0568] The server calculates rewards for data-providing users. Using reward management means, rewards are customized based on the amount of data provided by the user and their emotional labels. For example, users with a high number of "joy" emotional labels can be given additional rewards.

[0569] Step 12:

[0570] Users can check their benefits through a dedicated application. When a user opens the benefit confirmation screen within the application, current benefit information is retrieved from the server and displayed on the device screen.

[0571] Specific examples

[0572] For example, consider the case where User B agrees to provide data. User B's device collects SMS data every day at 2:00 AM, and personal information (phone number and name) is deleted using data filtering. The anonymized data is then converted into JSON format, and an emotion engine assigns an emotion label (e.g., "happy" or "sad"). This emotion-labeled data is compressed and sent to the server. The server receives the data, decompresses it, stores it, and analyzes it to use as training data for the AI ​​model. At the end of the month, the server calculates a discount on communication charges and additional data volume based on the amount of data provided by User B and the emotion label, and grants benefits. User B can view these benefits on the application.

[0573] In this way, the present invention realizes a series of processes: anonymizing, sentiment analysis, sending, receiving, and analyzing data collected from user devices, reflecting it in a generative AI model, and providing benefits to users. This provides a system that benefits both users and telecommunications carriers while thoroughly protecting personal information.

[0574] Example 2

[0575] 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."

[0576] In recent years, the spread of smart devices has led to a rapid increase in the amount of data generated by users. Systems that can effectively utilize this data and provide benefits to users are needed. However, an efficient system that consistently performs a series of processes, including data collection and anonymization, emotional labeling, secure data transmission, server analysis, and rewards management, has not yet been established. Furthermore, utilizing data while protecting user privacy is an important issue. Therefore, the present invention aims to provide a system that solves these problems.

[0577] The identification process by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes a data collection means for collecting data from user terminals, a data filtering means for anonymizing the data, a data transmission means for transmitting the anonymized data, a server reception means for receiving and storing the data, an emotion labeling means for assigning emotion labels to the anonymized data, a data analysis means for analyzing the labeled data and reflecting the results in a generative AI model, and a reward management means for providing users with rewards for providing the data. As a result, by consistently performing processes from data collection to analysis, emotion labeling, and reward management, it is possible to efficiently utilize user data and provide benefits to users.

[0578] "User terminal" means a device used by a user to generate and provide data, including a smartphone, tablet, or PC.

[0579] "Data Collection Measures" means functions or modules for collecting data generated by the User Device, which operate via specific applications or sensors.

[0580] "Data filtering measures" are functions or algorithms that remove or mask personally identifiable information from collected data.

[0581] "Data transmission means" refers to the functions and protocols for securely transmitting anonymized data to a server, and is often done via a communication line.

[0582] "Server receiving means" refers to a function or module for receiving data sent from a user terminal and saving it.

[0583] An "emotion labeling means" is a natural language processing algorithm or engine for assigning emotion labels to anonymized data.

[0584] "Data analysis means" refers to functions and modules that analyze received data and reflect it in the generative AI model.

[0585] The "reward management means" refers to a function or module for providing rewards to users in exchange for providing data, and is responsible for determining and notifying the content of the rewards.

[0586] A "generative AI model" is an artificial intelligence model that is trained using collected and analyzed data, and is used, for example, in natural language processing and sentiment analysis.

[0587] A "secure communication protocol" is a communication protocol that ensures security when sending and receiving data, and examples include HTTPS.

[0588] 1. Overall system configuration

[0589] The system of the present invention includes a user terminal, a server, and an interface for a user to check rewards for providing data. The user terminal includes a data collection means, a data filtering means, a data transmission means, and an emotion labeling means. Meanwhile, the server includes a server reception means, a data analysis means, and a reward management means.

[0590] 2. Data Collection and Filtering

[0591] Data collection is triggered when a user launches a specific application on their device and agrees to provide data. After confirming consent, the user's device periodically collects data such as SMS and browsing history. The collected data is anonymized by removing personally identifiable information using data filtering methods. This anonymized data is then converted to JSON format.

[0592] 3. Emotion Labeling

[0593] The anonymized data is then analyzed and labeled by a sentiment labeler, which uses natural language processing algorithms to identify emotions within the text data and assigns corresponding labels (e.g., happy, sad, anger, etc.). This sentiment-labeled data is also stored in JSON format.

[0594] 4. Data transmission

[0595] The anonymized data and emotion-labeled data are compressed by a data transmission means and transmitted to a server using a secure communication protocol. The device periodically transmits this data, allowing the server to store the latest data.

[0596] 5. Receiving and storing on the server

[0597] The server receives the data sent via the server receiving means, decompresses it, and stores it in a database. The server manages the received data and prepares it for analysis.

[0598] 6. Data analysis and reflection in AI models

[0599] The data analysis means on the server analyzes the stored data. The analyzed data is used as a training dataset for a generative AI model (e.g., ChatGPT). The emotion-labeled data is also used as a training dataset, contributing to improving the accuracy of the AI ​​model.

[0600] 7. Managing User Benefits

[0601] Users who agree to provide data can receive benefits based on the amount of data provided and the emotion label. The benefit management means manages these benefits and provides users with benefits such as discounts on communication charges or additional data.

[0602] 8. Check your benefits

[0603] Users can check their benefits through a dedicated application. When a user opens the benefit confirmation screen within the application, the latest benefit information is retrieved from the server and displayed.

[0604] Specific examples

[0605] For example, if a user agrees to provide data, the user's device will collect SMS data every day at 2:00 AM. Personally identifiable information (e.g., name, phone number) is removed from this data, anonymized, and converted into JSON format. An emotion labeling tool then assigns emotion labels such as "happy" or "sad." This labeled data is compressed and sent to a server via a secure communication protocol (e.g., HTTPS). The server receives the data, decompresses it, and stores it in a database. As a result of the analysis, new training data is added to the generative AI model, and the user can receive a discount on their communication charges at the end of the month, which can be confirmed in a dedicated app.

[0606] Prompt Sentence Examples

[0607] "SMS data collected from user devices is anonymized, emotion-labeled, converted to JSON format, and then sent to a server via a secure communication protocol. The received data is analyzed on the server and used as training data for the generative AI model. Rewards are then offered to users in the form of discounts on communication charges, which can then be viewed in the application."

[0608] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0609] Step 1: Start collecting data

[0610] The user launches the dedicated application on their device and agrees to provide data. This consent action initiates data collection. The input is the user's consent, and the output is the trigger for data collection. The specific action is when the user taps the "Data Provision Button."

[0611] Step 2: Collect data

[0612] The user device periodically collects data such as SMS and browsing history using a data collection method. The inputs are consent status and the specified data type, and the output is collected data. The specific operation is that the device scans and collects the target data at 2:00 AM every day.

[0613] Step 3: Filter and anonymize data

[0614] Personally identifiable information is removed from collected data using data filtering methods, resulting in anonymization. The input is collected raw data, and the output is anonymized data. Specifically, the process involves deleting the "Sato Taro" part from SMS data containing the name "Sato Taro" to anonymize it.

[0615] Step 4: Transforming the data and labeling it

[0616] The anonymized data is converted to JSON format, and then an emotion label is assigned to it by the emotion labeling means. The input is anonymized text data, and the output is JSON data with an emotion label assigned. The specific operation is to assign the label "joy" to the message "I'm so happy today!"

[0617] Step 5: Sending data

[0618] The emotion-labeled data is compressed and sent to the server by the data transmission means using a secure communication protocol. The input is emotion-labeled JSON data, and the output is a compressed data packet. The specific operation is to send the data to the server using HTTPS.

[0619] Step 6: Receiving and storing data

[0620] The server receives the data sent via the server receiving means, decompresses it, and stores it in the database. The input is the compressed data packet, and the output is the raw data stored in the database. The specific operation is to decompress the compressed data packet and insert it into the database.

[0621] Step 7: Analyze the data and incorporate it into the AI ​​model

[0622] The data analysis means in the server analyzes the stored data and reflects the results in the generative AI model. The input is the data to be analyzed, and the output is a training dataset for the generative AI model. The specific operation is to add emotion-labeled data to the dataset used for training.

[0623] Step 8: Manage and notify user benefits

[0624] The server calculates the rewards based on the amount of data provided by the user and the emotion label, and the reward management means notifies the user of the result. The input is the amount of data provided and the emotion label, and the output is the calculated reward information. The specific operation is to notify the user at the end of the month that "Your communication charges will be discounted by 20% depending on the amount of data provided."

[0625] Step 9: Check your benefits

[0626] The user checks the benefits through a dedicated application. The application retrieves the latest benefit information from the server and displays it. The input is a benefit confirmation request, and the output is the latest benefit information. The specific operation is for the user to select the "Benefits Confirmation" menu and check the current discount information and additional data volume.

[0627] (Application example 2)

[0628] 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."

[0629] Conventional advertising systems are unable to customize ads based on user emotions, making it difficult to deliver ads that effectively capture users' interests. Furthermore, from the perspective of protecting personal information, there was no system in place to safely manage collected data and provide users with special benefits.

[0630] The identification process by the identification 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 a data collection means for collecting data from user terminals, a data filtering means for anonymizing the data, and an emotion analysis means for assigning emotion labels to the anonymized data. This enables advertisement delivery based on user emotions.

[0631] A "user terminal" is a device such as a computer or mobile terminal used by a user.

[0632] A "data collection means" is a method or device for collecting data from a user terminal.

[0633] A "data filtering measure" is an algorithm or device used to remove or mask personally identifying information from collected data.

[0634] "Emotion analysis means" is a technology for identifying emotions in anonymized data and assigning emotion labels.

[0635] The "data transmission means" is a method or device for assigning emotion labels to anonymized data and transmitting it to a server.

[0636] The "server receiving means" is a method or device for receiving and storing data sent from a terminal.

[0637] "Data analysis means" refers to a method or device for analyzing received data and reflecting the results in the generative AI model.

[0638] The "reward management means" is a method or device for providing users with rewards for providing data and managing the rewards.

[0639] A "generative AI model" is an artificial intelligence model that is trained based on collected data.

[0640] "Emotion labels" are tags or marks that indicate a user's emotional state, obtained as a result of data analysis.

[0641] A "server" is a remote computing device that receives, analyzes, and stores data from multiple user terminals.

[0642] "Benefits" are rewards or benefits provided to users for actions such as providing data or viewing advertisements.

[0643] 1. Overall system configuration

[0644] This invention provides a system for realizing an emotion-intelligent advertising platform, including a user terminal, a server, and an interface for users to check rewards. The user terminal includes a data collection means, a data filtering means, an emotion analysis means, and a data transmission means. The server includes a server receiving means, a data analysis means, and a reward management means.

[0645] 2. Data Collection

[0646] When a user views an advertisement, the data collection means on the user's device uses the smartphone camera and built-in microphone to collect facial and voice data, which may reveal emotions such as interest, joy, indifference, and anger.

[0647] 3. Data Filtering

[0648] The collected data is anonymized using data filtering measures, such as face blurring, to remove any personally identifiable information, thereby protecting user privacy.

[0649] 4. Emotion analysis

[0650] The anonymized data is analyzed using natural language processing algorithms through sentiment analysis, for example using the Hugging Face transformers library to classify user emotions into labels such as "interested," "delighted," or "indifferent."

[0651] 5. Data Transmission

[0652] The emotion-labeled data is compressed by the data transmission means, encrypted using the HTTPS protocol, and sent to the server, ensuring data security.

[0653] 6. Data Reception and Analysis

[0654] The server receives the data sent via the server receiving means and stores it in a database. It then analyzes the data using the data analysis means as a training dataset for the generative AI model. In this process, it is possible to identify behavioral patterns and trends that are useful for ad delivery.

[0655] 7. Customizing and Offering Rewards

[0656] Users who consent to providing their data will be offered rewards using reward management tools. Rewards are customized based on emotional labels, so that, for example, users who are labeled as "interested" in an advertisement will receive additional rewards.

[0657] Specific examples

[0658] For example, let's say User A is viewing an advertisement on their smartphone. While viewing the advertisement, the smartphone's camera captures User A's facial expressions, and the built-in microphone collects their audio reactions. This data is blurred to anonymize personal information. Next, a natural language processing algorithm assigns an emotion label of "interest." The labeled data is securely sent to a server using HTTPS and analyzed there. The generated AI model uses this data to deliver new advertisements relevant to User A, thereby achieving more effective marketing. User A also earns points by viewing advertisements, which they can use to check for benefits such as discounts on communication charges within the app.

[0659] An example prompt might be something like, "Optimize the next ad shown to users based on the emotion label (e.g., interest, delight, indifference) they received when viewing a specific product ad."

[0660] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0661] Step 1:

[0662] The user device collects facial expression and voice data from the user while viewing the advertisement. Data is input using the camera and microphone of the smartphone or head-mounted display. This collected data becomes the basis for reflecting the user's emotions.

[0663] Step 2:

[0664] The collected data is then processed on the device using data filtering to remove personal information. Specifically, OpenCV is used to blur faces and anonymize the data. This process converts the raw data into anonymous data with personal information removed.

[0665] Step 3:

[0666] The anonymized data is then analyzed by a sentiment analysis tool, which uses Hugging Face's transformers library to run natural language processing algorithms. Based on the input anonymous data, the user's emotions are labeled as "interest," "happiness," "indifference," etc., generating emotion-labeled data. This data can then be used to deliver advertisements.

[0667] Step 4:

[0668] The emotion-labeled data is compressed by the data transmission means and sent to the server using the HTTPS protocol. The data is protected by a secure communication means and is therefore transported safely. In this step, the emotion-labeled data is transferred to the server.

[0669] Step 5:

[0670] The server receives the data sent from the terminal using the server receiving means. The received data is temporarily saved and stored in a database. The saved data is ready for analysis.

[0671] Step 6:

[0672] The data stored on the server undergoes detailed analysis by a data analysis tool. The analysis results are used as a training dataset for the generative AI model. The accuracy of the AI ​​model is improved based on the input emotion-labeled data. The results of the analysis are reflected in the ad distribution system.

[0673] Step 7:

[0674] The server uses a reward management means to provide rewards to users who agree to provide data. The rewards are customized based on the emotion label. For example, if a user is labeled as "interested," an additional reward is provided. The output data with the rewards is distributed to the user as a final notification, which the user can view in a dedicated application.

[0675] 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.

[0676] 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.

[0677] 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.

[0678] [Third embodiment]

[0679] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0680] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0681] 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).

[0682] 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.

[0683] 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.

[0684] 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).

[0685] 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.

[0686] 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.

[0687] 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.

[0688] 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.

[0689] 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.

[0690] 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."

[0691] The present invention relates to a system for collecting data from user terminals, filtering, anonymizing, transmitting, receiving, analyzing, and managing benefits. Specific embodiments for carrying out the present invention will be described below.

[0692] 1. Overall system configuration

[0693] The system of the present invention includes a user terminal, a server, and an interface for users to check rewards for providing data. The user terminal includes a data collection means, a data filtering means, and a data transmission means. Meanwhile, the server includes a server reception means, a data analysis means, and a reward management means.

[0694] 2. Data Collection and Filtering

[0695] A user launches a specific application on their device and agrees to provide data. Once this consent setting is confirmed, the user's device periodically triggers data collection methods to collect data such as SMS and browsing history. The collected data is anonymized by removing or masking personally identifiable information using data filtering methods. This anonymized data is then converted into a specified format, such as JSON.

[0696] 3. Sending and Receiving Data

[0697] The anonymized data is compressed by the data transmission means and transmitted to the server using a secure communication protocol. The server receives the data via the server reception means, decompresses it, and stores it in a database.

[0698] 4. Data analysis and reflection in AI models

[0699] The data stored on the server is analyzed by a data analysis means. The analyzed data is used as a training dataset for a generative AI model (such as ChatGPT) and is used to improve the accuracy of the model.

[0700] 5. Managing User Benefits

[0701] Users who agree to provide data will receive benefits such as discounts on communication charges and additional data volume depending on the amount of data provided. These benefits are calculated and managed by the server's benefit management means and reflected in the user's communication plan. Users can check the details of the benefits they have been granted through a dedicated application.

[0702] Specific examples

[0703] For example, consider the case where User A agrees to provide data. User A's device collects SMS data every day at 2:00 AM and removes phone numbers and names as part of personal information filtering. This filtered data is then converted into JSON format, compressed, and sent to the server. The server receives and stores this data, and uses it as training data for the AI ​​model. At the end of the month, the server calculates the amount of data provided by User A and grants discounts on communication charges and additional data volume. User A can check these benefits on the application.

[0704] The above is a specific embodiment of the present invention. This embodiment allows carriers to efficiently collect big data while providing substantial benefits to users. This system also meets the requirements for personal information protection, realizing a mechanism that benefits both users and carriers.

[0705] The processing flow will be explained below.

[0706] Step 1:

[0707] The user launches a dedicated application on the device and selects the option to consent to the provision of data. Once consent is confirmed, the device saves this setting in local storage and sends a consent notification to the server.

[0708] Step 2:

[0709] The device triggers a background task at the set interval (e.g., 2:00 AM every day) to begin data collection. The device retrieves and collects new messages from the SMS application.

[0710] Step 3:

[0711] The device scans the content of collected messages for personally identifiable information like phone numbers and names, and uses regular expressions to remove or mask this information to anonymize the data.

[0712] Step 4:

[0713] The device converts the anonymized data into JSON format and temporarily stores it locally, where it is then appropriately prepared as a file by the data filtering method.

[0714] Step 5:

[0715] The terminal compresses the filtered data using a method such as gzip and transmits it to the server using a secure communication protocol (e.g., HTTPS). The data transmission means manages this communication.

[0716] Step 6:

[0717] The server receives the HTTP request, decompresses the sent compressed data, verifies the data format and content for errors, and stores it in a local database.

[0718] Step 7:

[0719] The server analyzes the stored data. Using the data analysis means, the text data is processed with a natural language processing algorithm and necessary metadata (collection date and time, device ID, etc.) is added.

[0720] Step 8:

[0721] The server adds the analysis results to the training dataset of the generative AI model, which is then used to improve the accuracy of the generative AI model and develop new functions.

[0722] Step 9:

[0723] The server calculates the benefits for data-providing users and reflects them in the users' communication plans using a benefits management tool, often on a monthly basis.

[0724] Step 10:

[0725] The user checks the benefits through a dedicated application. The user opens the benefit confirmation screen on the application and obtains current benefit information from the server.

[0726] Step 11:

[0727] The device displays the benefit information it has obtained from the server on the user interface, allowing the user to check the benefits, such as discounts on communication charges and additional data volume.

[0728] Example 1

[0729] 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."

[0730] In modern information and communication technology, anonymization and efficient use of data collected from users are important challenges. There is also a need to improve the quantity and quality of collected data by offering appropriate rewards for the data provided by users. However, a system that efficiently anonymizes, compresses, securely transmits collected data, and then analyzes and manages rewards has yet to be fully developed. The present invention aims to solve these challenges and bring benefits to both users and service providers.

[0731] 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.

[0732] In this invention, the server includes a data collection means, a data filtering means, a data transmission means, a server receiving means, a data analysis means, and a reward management means. This allows data collected from user terminals to be converted into JSON format, compressed, and securely transmitted. Furthermore, by providing a means for setting consent to data provision via the user interface, a mechanism is provided for users to clearly consent to the provision of their own data, realizing a system that can provide appropriate rewards to users while thoroughly protecting personal information.

[0733] "Data collection means" refers to the functions and mechanisms for collecting data from user terminals.

[0734] "Data filtering measures" are functions or algorithms used to remove or mask personally identifiable information from collected data.

[0735] "Data transmission means" refers to the functions and protocols for transmitting anonymized data to a server.

[0736] "Server receiving means" refers to the function or mechanism by which the server receives and stores data.

[0737] "Data analysis means" refers to functions and algorithms for analyzing received data, which can be reflected in the generative AI model.

[0738] The "reward management means" refers to a function or module for providing users with rewards for providing data.

[0739] "Format conversion and compression means" refers to functions and algorithms for converting collected data into a specified format (e.g., JSON format) and compressing it.

[0740] "Consent setting means" refers to a function or interface that allows a user to set consent to the provision of data.

[0741] The present invention relates to a system for collecting data from user terminals, filtering, anonymizing, transmitting, receiving, analyzing, and managing benefits. Specific embodiments for carrying out the present invention will be described below.

[0742] 1. Overall system configuration

[0743] The system includes a user terminal, a server, and a user interface for users to check the rewards for providing data. The user terminal is equipped with a data collection means, a data filtering means, and a data transmission means. The server side includes a server reception means, a data analysis means, and a reward management means.

[0744] 2. Examples of Data Collection and Filtering

[0745] The user launches a dedicated application and agrees to provide data. Once consent is confirmed, the user's device periodically triggers data collection methods to collect data such as SMS and browsing history. For example, if a user configures SMS data collection at 2:00 AM every day, the data will be collected at the specified time. The collected data is anonymized by deleting or masking personal information. This anonymized data is then converted into JSON format.

[0746] 3. Examples of sending and receiving data

[0747] The anonymized data is compressed by the data transmission means and transmitted to the server using a secure communication protocol (such as HTTPS). The server receives the data via the server reception means, decompresses it, and stores it in a database.

[0748] 4. Analyzing the data and reflecting it in the generative AI model

[0749] The data stored on the server is analyzed by a data analysis means. The analyzed data is used as a training dataset for a generative AI model (e.g., ChatGPT). This training data improves the accuracy of the AI ​​model.

[0750] 5. Examples of User Benefit Management

[0751] Users who agree to provide data will receive benefits such as discounts on communication charges and additional data volume depending on the amount of data provided. These benefits are calculated and managed by the server's benefit management means and reflected in the user's communication plan. Users can check the details of the benefits they have been granted through a dedicated application.

[0752] Prompt Sentence Examples

[0753] Possible input prompts for a generative AI model include:

[0754] "Using the SMS data provided by users, please analyze their usage patterns."

[0755] "Guess your interests based on your browsing history."

[0756] By integrating the above functions, this system efficiently collects data from users, analyzes it, and uses it to train AI models. Furthermore, by offering appropriate benefits to users in exchange for providing their data, it has created a system that benefits both users and service providers.

[0757] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0758] Step 1:

[0759] The user launches the dedicated application and agrees to provide data.

[0760] What happens: The user opens the application, agrees to the terms and conditions for providing data, and then checks a specific box, after which their consent settings are saved within the app.

[0761] Input: User consent settings.

[0762] Output: The consent settings are saved and the next step is triggered.

[0763] Step 2:

[0764] The terminal periodically collects data using the data collection means.

[0765] Specific behavior: After consent is confirmed, the device's background service will be automatically triggered at a specific time (e.g., 2:00 AM) to collect necessary data, such as SMS data and browsing history.

[0766] Input: User consent settings and current time.

[0767] Output: Collected data (e.g. SMS, browsing history).

[0768] Step 3:

[0769] It filters the data collected by the device and anonymizes personal information.

[0770] What it does: Collected data is filtered to remove or mask any personally identifiable information, such as phone numbers and names, thereby anonymizing the data.

[0771] Input: Collected data.

[0772] Output: Anonymized data.

[0773] Step 4:

[0774] The device converts the anonymized data into JSON format, compresses it, and sends it to the server.

[0775] Specific operation: After the anonymized data is converted into JSON format, the data is compressed using a format such as ZIP and sent to the server using a secure communication protocol (e.g., HTTPS).

[0776] Input: Anonymized data.

[0777] Output: Compressed JSON data is sent to the server.

[0778] Step 5:

[0779] The server receives the data, unpacks it, and stores it in a database.

[0780] What happens: The server's API endpoint receives the data, unpacks it, and stores it in the database.

[0781] Input: Compressed JSON data.

[0782] Output: Data stored in a database.

[0783] Step 6:

[0784] The server analyzes the data using a data analysis means and reflects it in the training dataset of the generative AI model.

[0785] What it does: The stored data is analyzed and used as a training dataset for generative AI models (e.g., ChatGPT), using statistical methods and machine learning algorithms.

[0786] Input: Data stored in a database.

[0787] Output: Parsed data and a training dataset for a generative AI model.

[0788] Step 7:

[0789] The server calculates the amount of data provided by the user and grants rewards.

[0790] Specific operation: The server's reward management module aggregates the amount of data provided by each user and generates rewards such as discounts on communication charges or additional data volume based on the results.

[0791] Input: Parsed data and user-provided data volume.

[0792] Output: Benefit information (e.g., discounts on communication charges, additional data volume).

[0793] Step 8:

[0794] The user checks for offers through the application.

[0795] What it does: Users can open the "Rewards Information" section of the app and view details of the rewards they have been granted.

[0796] Input: Benefit information.

[0797] Output: Benefit details for user to review.

[0798] (Application example 1)

[0799] 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."

[0800] There is a need to improve production line efficiency by efficiently collecting and analyzing operational information, maintenance history, and defect information from robots used in factories. However, to effectively collect and analyze this data, it is essential to protect personal information and anonymize the data. It is also important to provide appropriate benefits to factory operators. This is expected to increase the efficiency of robot operations and improve the accuracy of predictive maintenance and defect detection.

[0801] 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.

[0802] In this invention, the server includes a data collection means for collecting data from user terminals, a data filtering means for anonymizing the data, a data transmission means for transmitting the anonymized data to the server, a server reception means for receiving and storing the data, a data analysis means for analyzing the data and reflecting it in a generative AI model, a reward management means for providing users with rewards for providing the data, and a factory robot having the function of collecting, anonymizing, compressing, and transmitting machine operation data, defect information, maintenance history, etc. This enables improved operational efficiency of the factory robot and improved accuracy of predictive maintenance and defect detection.

[0803] - "User terminal" means a device that has the ability to collect data and transmit it to a server.

[0804] "Data collection means" is a function for collecting necessary information from the terminal.

[0805] "Data filtering measures" are algorithms that remove or anonymize certain information from collected data.

[0806] The "data transmission means" is a function for transmitting anonymized data to a server.

[0807] The "server receiving means" is a function for receiving and storing transmitted data.

[0808] "Data analysis means" is a function for analyzing received data and reflecting it in the AI ​​model.

[0809] The "reward management means" is a function for calculating rewards for providing data and providing them to users.

[0810] A "factory robot" is a mechanical device that automates work within a factory and has the function of collecting operational data, maintenance history, and defect information.

[0811] A "generative AI model" is an artificial intelligence model that is trained based on analyzed data.

[0812] A "prompt" is an instruction or training example to be input into a generative AI model.

[0813] MODE FOR CARRYING OUT THE INVENTION

[0814] The present invention relates to a system that improves the efficiency of production lines by collecting and analyzing operation information, maintenance history, and defect information of robots used in factories. This system includes a user terminal, factory robots, a server, and an interface for privilege management.

[0815] Overall system configuration

[0816] The system of the present invention comprises the following components:

[0817] 1. User terminal: has data collection means, data filtering means, and data transmission means.

[0818] 2. Factory robots: They have the ability to collect machine operation data, malfunction information, maintenance history, etc., and transmit this information in an anonymized form.

[0819] 3. Server: Includes server receiving means, data analysis means, and benefit management means.

[0820] 4. Interface: Includes a means for users to check the rewards for the data they provide.

[0821] Data Collection and Filtering

[0822] The user (factory operator) launches a specific application on their device and agrees to provide data. Once this consent is confirmed, the factory robots will periodically collect data such as operation data, malfunction information, and maintenance history. The collected data is anonymized using data filtering methods.

[0823] Sending and Receiving Data

[0824] The anonymized data is compressed by the data transmission means and sent to the server. A secure protocol (e.g., SSL / TLS) is used for communication. The server receives the data via the server reception means, decompresses it, and stores it in a database.

[0825] Analyzing data and reflecting it in AI models

[0826] The data stored on the server is analyzed by a data analysis tool. The analyzed data is used as training data for a generative AI model (such as ChatGPT) and is used to improve the accuracy of the model.

[0827] Managing User Benefits

[0828] Users who agree to provide data will receive benefits according to the amount of data provided. These benefits include discounts on communication charges and additional data volume. These benefits are calculated and managed by a benefit management system and reflected in the user's communication plan. Users can check the details of the benefits they have received through a dedicated application.

[0829] Specific examples

[0830] For example, consider the case where a factory operator agrees to provide data. Factory robots collect operational data at a specific time each day and remove any personally identifiable information (such as robot ID and factory name). This filtered data is then converted into JSON format, compressed, and sent to a server. The server receives and stores this data, and uses it as training data for AI models. At the end of the month, the server calculates the amount of data provided by the user and awards them rewards. The user can view these rewards on the application.

[0831] Hardware and software used

[0832] Hardware: Factory robots, sensor devices

[0833] Software: Python, zlib library, requests library

[0834] Prompt Sentence Examples

[0835] "Design an application that collects operational data from factory robots, anonymizes it, and transmits it to a central server using a secure communication protocol. The central server analyzes the data and uses it as training data for a generative AI model."

[0836] This invention enables factory operators to carry out efficient operation and maintenance based on robot operational data, improving the accuracy of AI models.

[0837] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0838] Step 1:

[0839] The user device confirms consent to provide data. Once this consent is confirmed, the user device starts the process to collect the data. The input is the user's confirmed consent, and the output is the trigger for the data collection process. The specific behavior is that the user launches the application and clicks the consent button.

[0840] Step 2:

[0841] Factory robots collect operational data, malfunction information, maintenance history, etc. The collected data is sent as raw data to a user terminal. The inputs include the factory robot's operational data, malfunction records, and maintenance history, and the output is sent to the user terminal. Specifically, the data is collected using a sensor device and sent to the user terminal.

[0842] Step 3:

[0843] The collected data is filtered and anonymized on the user's device. Personally identifiable information (such as robot ID and factory name) is removed or masked. The input is the raw data sent by the factory robots, and the output is anonymized data. Specifically, an algorithm is run to remove identifying information.

[0844] Step 4:

[0845] The anonymized data is converted to JSON format and then compressed. This process converts the data into a transportable format. The input is the anonymized data, and the output is the data that has been converted to JSON format and compressed. The specific operations include converting the data to JSON format and then compressing it.

[0846] Step 5:

[0847] The compressed data is sent to the server using a secure communication protocol (e.g. SSL / TLS). The input is the compressed data, and the output is the securely transmitted data. The specific operation is to perform a request to send the data using the HTTPS protocol.

[0848] Step 6:

[0849] The server receives the transmitted data, decompresses it, and stores it in a database, which also serves as a repository for other data. The input is the compressed data, and the output is the decompressed data stored in the database. Specific operations include decompressing the data and writing it to the database.

[0850] Step 7:

[0851] The server analyzes the stored data and applies it to the generative AI model. The analyzed data is used as training data for the AI ​​model. The input is the raw data stored in the database, and the output is a trained AI model. Specific operations include applying a data analysis algorithm and training the AI ​​model.

[0852] Step 8:

[0853] The server grants rewards to users (factory operators) based on the amount of data provided. These rewards include discounts on communication charges and additional data volume. The input is the analyzed amount of data provided, and the output is the rewards granted. Specifically, the reward management means calculates the amount of data provided and applies the rewards to the user account.

[0854] Through these steps, the invention can efficiently and safely collect and analyze operational data from factory robots and use it to train AI models. Furthermore, by offering appropriate rewards to users for providing data, user satisfaction can be increased.

[0855] 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.

[0856] The present invention relates to a system that collects data from user terminals, anonymizes, transmits, receives, and analyzes the data, and further combines it with an emotion engine that recognizes the user's emotions. Specific embodiments for carrying out the present invention will be described below.

[0857] 1. Overall system configuration

[0858] The system of the present invention includes a user terminal, a server, and an interface for users to check rewards for providing data. The user terminal includes a data collection means, a data filtering means, a data transmission means, and an emotion engine. Meanwhile, the server includes a server reception means, a data analysis means, and a reward management means.

[0859] 2. Data Collection and Filtering

[0860] A user launches a specific application on their device and agrees to provide data. Once consent is confirmed, the user's device periodically triggers a data collection mechanism to collect data such as SMS and browsing history. The collected data is then anonymized by removing any personally identifiable information through a data filtering mechanism. This anonymized data is then converted into JSON format.

[0861] 3. Operation of the Emotion Engine

[0862] The anonymized data is then analyzed and labeled by a sentiment engine, which uses natural language processing algorithms to identify emotions within the text data and assigns corresponding labels (e.g., happy, sad, anger, etc.). This sentiment-labeled data is also stored in JSON format.

[0863] 4. Sending and Receiving Data

[0864] The anonymized data and emotion-labeled data are compressed by the data transmission means and transmitted to the server using a secure communication protocol. The server receives the data via the server reception means, decompresses it, and stores it in a database.

[0865] 5. Data analysis and reflection in AI models

[0866] The data stored on the server is analyzed by a data analysis tool. The analyzed data is used as a training dataset for a generative AI model (e.g., ChatGPT). Emotion-labeled data is also used as a training dataset, contributing to improving the accuracy of the AI ​​model.

[0867] 6. Managing User Benefits

[0868] Users who agree to provide data will receive benefits such as discounts on communication charges or additional data depending on the amount of data they provide. The benefit management method can also customize benefits based on emotion labels. For example, additional benefits can be provided to users with a high number of joy emotion labels.

[0869] 7. Check your benefits

[0870] Users can check their benefits through a dedicated application. When a user opens the benefit confirmation screen within the application, current benefit information is retrieved from the server and displayed.

[0871] Specific examples

[0872] For example, consider the case where User B agrees to provide data. User B's device collects SMS data every day at 2:00 AM and removes phone numbers and names as part of personal information filtering. This filtered data is then converted into JSON format, and an emotion engine assigns emotion labels to it. The emotion-labeled data is compressed and sent to the server, which receives and stores it. The server analyzes the received data and uses it as training data for the AI ​​model. At the end of the month, the server calculates discounts on communication charges and additional data volume based on User B's data provision volume and emotion labels, and grants benefits. User B can view these benefits on the application.

[0873] The above is a specific embodiment of the present invention, which allows carriers to efficiently collect big data while providing substantial benefits to users. The system satisfies the requirements for personal information protection and also improves the user experience by providing rewards that take user sentiment into account.

[0874] The processing flow will be explained below.

[0875] Step 1:

[0876] The user launches a dedicated application on the device and selects the option to consent to the provision of data. Once consent is confirmed, the device saves this setting in local storage and sends a consent notification to the server.

[0877] Step 2:

[0878] The device triggers a background task at a set interval (e.g., every day at 2:00 AM) to begin data collection. The device retrieves new data from SMS applications and browsing history.

[0879] Step 3:

[0880] The data collected by the device is processed using data filtering methods to detect personally identifiable information such as phone numbers, names, etc. This information is then removed or masked using regular expressions to anonymize the data.

[0881] Step 4:

[0882] The device converts the anonymized data into JSON format and temporarily stores it locally.

[0883] Step 5:

[0884] The device then runs an emotion engine that analyzes the anonymized data, using natural language processing algorithms to identify emotions within the text data and assigns them emotion labels such as "joy," "sadness," or "anger."

[0885] Step 6:

[0886] The emotion-labeled data is also stored in JSON format and compressed by the data transmission method.

[0887] Step 7:

[0888] The device sends the compressed data to the server using a secure communication protocol (e.g., HTTPS). An HTTP POST request is used for transmission.

[0889] Step 8:

[0890] The server receives the HTTP request, decompresses the compressed data, verifies the data format and content, and stores it in a local database.

[0891] Step 9:

[0892] The server analyzes the stored data using data analysis tools, and the analysis results are used as a training dataset for a generative AI model (e.g., ChatGPT).

[0893] Step 10:

[0894] The emotion-labeled data will also be analyzed and used as training data for the AI ​​model, which is expected to improve the accuracy of the AI ​​model's emotion recognition ability.

[0895] Step 11:

[0896] The server calculates rewards for data-providing users. Using reward management means, rewards are customized based on the amount of data provided by the user and their emotional labels. For example, users with a high number of "joy" emotional labels can be given additional rewards.

[0897] Step 12:

[0898] Users can check their benefits through a dedicated application. When a user opens the benefit confirmation screen within the application, current benefit information is retrieved from the server and displayed on the device screen.

[0899] Specific examples

[0900] For example, consider the case where User B agrees to provide data. User B's device collects SMS data every day at 2:00 AM, and personal information (phone number and name) is deleted using data filtering. The anonymized data is then converted into JSON format, and an emotion engine assigns an emotion label (e.g., "happy" or "sad"). This emotion-labeled data is compressed and sent to the server. The server receives the data, decompresses it, stores it, and analyzes it to use as training data for the AI ​​model. At the end of the month, the server calculates a discount on communication charges and additional data volume based on the amount of data provided by User B and the emotion label, and grants benefits. User B can view these benefits on the application.

[0901] In this way, the present invention realizes a series of processes: anonymizing, sentiment analysis, sending, receiving, and analyzing data collected from user devices, reflecting it in a generative AI model, and providing benefits to users. This provides a system that benefits both users and telecommunications carriers while thoroughly protecting personal information.

[0902] Example 2

[0903] 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."

[0904] In recent years, the spread of smart devices has led to a rapid increase in the amount of data generated by users. Systems that can effectively utilize this data and provide benefits to users are needed. However, an efficient system that consistently performs a series of processes, including data collection and anonymization, emotional labeling, secure data transmission, server analysis, and rewards management, has not yet been established. Furthermore, utilizing data while protecting user privacy is an important issue. Therefore, the present invention aims to provide a system that solves these problems.

[0905] The identification process by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes a data collection means for collecting data from user terminals, a data filtering means for anonymizing the data, a data transmission means for transmitting the anonymized data, a server reception means for receiving and storing the data, an emotion labeling means for assigning emotion labels to the anonymized data, a data analysis means for analyzing the labeled data and reflecting the results in a generative AI model, and a reward management means for providing users with rewards for providing the data. As a result, by consistently performing processes from data collection to analysis, emotion labeling, and reward management, it is possible to efficiently utilize user data and provide benefits to users.

[0906] "User terminal" means a device used by a user to generate and provide data, including a smartphone, tablet, or PC.

[0907] "Data Collection Measures" means functions or modules for collecting data generated by the User Device, which operate via specific applications or sensors.

[0908] "Data filtering measures" are functions or algorithms that remove or mask personally identifiable information from collected data.

[0909] "Data transmission means" refers to the functions and protocols for securely transmitting anonymized data to a server, and is often done via a communication line.

[0910] "Server receiving means" refers to a function or module for receiving data sent from a user terminal and saving it.

[0911] An "emotion labeling means" is a natural language processing algorithm or engine for assigning emotion labels to anonymized data.

[0912] "Data analysis means" refers to functions and modules that analyze received data and reflect it in the generative AI model.

[0913] The "reward management means" refers to a function or module for providing rewards to users in exchange for providing data, and is responsible for determining and notifying the content of the rewards.

[0914] A "generative AI model" is an artificial intelligence model that is trained using collected and analyzed data, and is used, for example, in natural language processing and sentiment analysis.

[0915] A "secure communication protocol" is a communication protocol that ensures security when sending and receiving data, and examples include HTTPS.

[0916] 1. Overall system configuration

[0917] The system of the present invention includes a user terminal, a server, and an interface for a user to check rewards for providing data. The user terminal includes a data collection means, a data filtering means, a data transmission means, and an emotion labeling means. Meanwhile, the server includes a server reception means, a data analysis means, and a reward management means.

[0918] 2. Data Collection and Filtering

[0919] Data collection is triggered when a user launches a specific application on their device and agrees to provide data. After confirming consent, the user's device periodically collects data such as SMS and browsing history. The collected data is anonymized by removing personally identifiable information using data filtering methods. This anonymized data is then converted to JSON format.

[0920] 3. Emotion Labeling

[0921] The anonymized data is then analyzed and labeled by a sentiment labeler, which uses natural language processing algorithms to identify emotions within the text data and assigns corresponding labels (e.g., happy, sad, anger, etc.). This sentiment-labeled data is also stored in JSON format.

[0922] 4. Data transmission

[0923] The anonymized data and emotion-labeled data are compressed by a data transmission means and transmitted to a server using a secure communication protocol. The device periodically transmits this data, allowing the server to store the latest data.

[0924] 5. Receiving and storing on the server

[0925] The server receives the data sent via the server receiving means, decompresses it, and stores it in a database. The server manages the received data and prepares it for analysis.

[0926] 6. Data analysis and reflection in AI models

[0927] The data analysis means on the server analyzes the stored data. The analyzed data is used as a training dataset for a generative AI model (e.g., ChatGPT). The emotion-labeled data is also used as a training dataset, contributing to improving the accuracy of the AI ​​model.

[0928] 7. Managing User Benefits

[0929] Users who agree to provide data can receive benefits based on the amount of data provided and the emotion label. The benefit management means manages these benefits and provides users with benefits such as discounts on communication charges or additional data.

[0930] 8. Check your benefits

[0931] Users can check their benefits through a dedicated application. When a user opens the benefit confirmation screen within the application, the latest benefit information is retrieved from the server and displayed.

[0932] Specific examples

[0933] For example, if a user agrees to provide data, the user's device will collect SMS data every day at 2:00 AM. Personally identifiable information (e.g., name, phone number) is removed from this data, anonymized, and converted into JSON format. An emotion labeling tool then assigns emotion labels such as "happy" or "sad." This labeled data is compressed and sent to a server via a secure communication protocol (e.g., HTTPS). The server receives the data, decompresses it, and stores it in a database. As a result of the analysis, new training data is added to the generative AI model, and the user can receive a discount on their communication charges at the end of the month, which can be confirmed in a dedicated app.

[0934] Prompt Sentence Examples

[0935] "SMS data collected from user devices is anonymized, emotion-labeled, converted to JSON format, and then sent to a server via a secure communication protocol. The received data is analyzed on the server and used as training data for the generative AI model. Rewards are then offered to users in the form of discounts on communication charges, which can then be viewed in the application."

[0936] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0937] Step 1: Start collecting data

[0938] The user launches the dedicated application on their device and agrees to provide data. This consent action initiates data collection. The input is the user's consent, and the output is the trigger for data collection. The specific action is when the user taps the "Data Provision Button."

[0939] Step 2: Collect data

[0940] The user device periodically collects data such as SMS and browsing history using a data collection method. The inputs are consent status and the specified data type, and the output is collected data. The specific operation is that the device scans and collects the target data at 2:00 AM every day.

[0941] Step 3: Filter and anonymize data

[0942] Personally identifiable information is removed from collected data using data filtering methods, resulting in anonymization. The input is collected raw data, and the output is anonymized data. Specifically, the process involves deleting the "Sato Taro" part from SMS data containing the name "Sato Taro" to anonymize it.

[0943] Step 4: Transforming the data and labeling it

[0944] The anonymized data is converted to JSON format, and then an emotion label is assigned to it by the emotion labeling means. The input is anonymized text data, and the output is JSON data with an emotion label assigned. The specific operation is to assign the label "joy" to the message "I'm so happy today!"

[0945] Step 5: Sending data

[0946] The emotion-labeled data is compressed and sent to the server by the data transmission means using a secure communication protocol. The input is emotion-labeled JSON data, and the output is a compressed data packet. The specific operation is to send the data to the server using HTTPS.

[0947] Step 6: Receiving and storing data

[0948] The server receives the data sent via the server receiving means, decompresses it, and stores it in the database. The input is the compressed data packet, and the output is the raw data stored in the database. The specific operation is to decompress the compressed data packet and insert it into the database.

[0949] Step 7: Analyze the data and incorporate it into the AI ​​model

[0950] The data analysis means in the server analyzes the stored data and reflects the results in the generative AI model. The input is the data to be analyzed, and the output is a training dataset for the generative AI model. The specific operation is to add emotion-labeled data to the dataset used for training.

[0951] Step 8: Manage and notify user benefits

[0952] The server calculates the rewards based on the amount of data provided by the user and the emotion label, and the reward management means notifies the user of the result. The input is the amount of data provided and the emotion label, and the output is the calculated reward information. The specific operation is to notify the user at the end of the month that "Your communication charges will be discounted by 20% depending on the amount of data provided."

[0953] Step 9: Check your benefits

[0954] The user checks the benefits through a dedicated application. The application retrieves the latest benefit information from the server and displays it. The input is a benefit confirmation request, and the output is the latest benefit information. The specific operation is for the user to select the "Benefits Confirmation" menu and check the current discount information and additional data volume.

[0955] (Application example 2)

[0956] 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."

[0957] Conventional advertising systems are unable to customize ads based on user emotions, making it difficult to deliver ads that effectively capture users' interests. Furthermore, from the perspective of protecting personal information, there was no system in place to safely manage collected data and provide users with special benefits.

[0958] The identification process by the identification 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 a data collection means for collecting data from user terminals, a data filtering means for anonymizing the data, and an emotion analysis means for assigning emotion labels to the anonymized data. This enables advertisement delivery based on user emotions.

[0959] A "user terminal" is a device such as a computer or mobile terminal used by a user.

[0960] A "data collection means" is a method or device for collecting data from a user terminal.

[0961] A "data filtering measure" is an algorithm or device used to remove or mask personally identifying information from collected data.

[0962] "Emotion analysis means" is a technology for identifying emotions in anonymized data and assigning emotion labels.

[0963] The "data transmission means" is a method or device for assigning emotion labels to anonymized data and transmitting it to a server.

[0964] The "server receiving means" is a method or device for receiving and storing data sent from a terminal.

[0965] "Data analysis means" refers to a method or device for analyzing received data and reflecting the results in the generative AI model.

[0966] The "reward management means" is a method or device for providing users with rewards for providing data and managing the rewards.

[0967] A "generative AI model" is an artificial intelligence model that is trained based on collected data.

[0968] "Emotion labels" are tags or marks that indicate a user's emotional state, obtained as a result of data analysis.

[0969] A "server" is a remote computing device that receives, analyzes, and stores data from multiple user terminals.

[0970] "Benefits" are rewards or benefits provided to users for actions such as providing data or viewing advertisements.

[0971] 1. Overall system configuration

[0972] This invention provides a system for realizing an emotion-intelligent advertising platform, including a user terminal, a server, and an interface for users to check rewards. The user terminal includes a data collection means, a data filtering means, an emotion analysis means, and a data transmission means. The server includes a server receiving means, a data analysis means, and a reward management means.

[0973] 2. Data Collection

[0974] When a user views an advertisement, the data collection means on the user's device uses the smartphone camera and built-in microphone to collect facial and voice data, which may reveal emotions such as interest, joy, indifference, and anger.

[0975] 3. Data Filtering

[0976] The collected data is anonymized using data filtering measures, such as face blurring, to remove any personally identifiable information, thereby protecting user privacy.

[0977] 4. Emotion analysis

[0978] The anonymized data is analyzed using natural language processing algorithms through sentiment analysis, for example using the Hugging Face transformers library to classify user emotions into labels such as "interested," "delighted," or "indifferent."

[0979] 5. Data Transmission

[0980] The emotion-labeled data is compressed by the data transmission means, encrypted using the HTTPS protocol, and sent to the server, ensuring data security.

[0981] 6. Data Reception and Analysis

[0982] The server receives the data sent via the server receiving means and stores it in a database. It then analyzes the data using the data analysis means as a training dataset for the generative AI model. In this process, it is possible to identify behavioral patterns and trends that are useful for ad delivery.

[0983] 7. Customizing and Offering Rewards

[0984] Users who consent to providing their data will be offered rewards using reward management tools. Rewards are customized based on emotional labels, so that, for example, users who are labeled as "interested" in an advertisement will receive additional rewards.

[0985] Specific examples

[0986] For example, let's say User A is viewing an advertisement on their smartphone. While viewing the advertisement, the smartphone's camera captures User A's facial expressions, and the built-in microphone collects their audio reactions. This data is blurred to anonymize personal information. Next, a natural language processing algorithm assigns an emotion label of "interest." The labeled data is securely sent to a server using HTTPS and analyzed there. The generated AI model uses this data to deliver new advertisements relevant to User A, thereby achieving more effective marketing. User A also earns points by viewing advertisements, which they can use to check for benefits such as discounts on communication charges within the app.

[0987] An example prompt might be something like, "Optimize the next ad shown to users based on the emotion label (e.g., interest, delight, indifference) they received when viewing a specific product ad."

[0988] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0989] Step 1:

[0990] The user device collects facial expression and voice data from the user while viewing the advertisement. Data is input using the camera and microphone of the smartphone or head-mounted display. This collected data becomes the basis for reflecting the user's emotions.

[0991] Step 2:

[0992] The collected data is then processed on the device using data filtering to remove personal information. Specifically, OpenCV is used to blur faces and anonymize the data. This process converts the raw data into anonymous data with personal information removed.

[0993] Step 3:

[0994] The anonymized data is then analyzed by a sentiment analysis tool, which uses Hugging Face's transformers library to run natural language processing algorithms. Based on the input anonymous data, the user's emotions are labeled as "interest," "happiness," "indifference," etc., generating emotion-labeled data. This data can then be used to deliver advertisements.

[0995] Step 4:

[0996] The emotion-labeled data is compressed by the data transmission means and sent to the server using the HTTPS protocol. The data is protected by a secure communication means and is therefore transported safely. In this step, the emotion-labeled data is transferred to the server.

[0997] Step 5:

[0998] The server receives the data sent from the terminal using the server receiving means. The received data is temporarily saved and stored in a database. The saved data is ready for analysis.

[0999] Step 6:

[1000] The data stored on the server undergoes detailed analysis by a data analysis tool. The analysis results are used as a training dataset for the generative AI model. The accuracy of the AI ​​model is improved based on the input emotion-labeled data. The results of the analysis are reflected in the ad distribution system.

[1001] Step 7:

[1002] The server uses a reward management means to provide rewards to users who agree to provide data. The rewards are customized based on the emotion label. For example, if a user is labeled as "interested," an additional reward is provided. The output data with the rewards is distributed to the user as a final notification, which the user can view in a dedicated application.

[1003] 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.

[1004] 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.

[1005] 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.

[1006] [Fourth embodiment]

[1007] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1008] 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.

[1009] 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).

[1010] 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.

[1011] 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.

[1012] 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).

[1013] 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.

[1014] 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.

[1015] 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.

[1016] 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.

[1017] 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.

[1018] 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.

[1019] 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."

[1020] The present invention relates to a system for collecting data from user terminals, filtering, anonymizing, transmitting, receiving, analyzing, and managing benefits. Specific embodiments for carrying out the present invention will be described below.

[1021] 1. Overall system configuration

[1022] The system of the present invention includes a user terminal, a server, and an interface for users to check rewards for providing data. The user terminal includes a data collection means, a data filtering means, and a data transmission means. Meanwhile, the server includes a server reception means, a data analysis means, and a reward management means.

[1023] 2. Data Collection and Filtering

[1024] A user launches a specific application on their device and agrees to provide data. Once this consent setting is confirmed, the user's device periodically triggers data collection methods to collect data such as SMS and browsing history. The collected data is anonymized by removing or masking personally identifiable information using data filtering methods. This anonymized data is then converted into a specified format, such as JSON.

[1025] 3. Sending and Receiving Data

[1026] The anonymized data is compressed by the data transmission means and transmitted to the server using a secure communication protocol. The server receives the data via the server reception means, decompresses it, and stores it in a database.

[1027] 4. Data analysis and reflection in AI models

[1028] The data stored on the server is analyzed by a data analysis means. The analyzed data is used as a training dataset for a generative AI model (such as ChatGPT) and is used to improve the accuracy of the model.

[1029] 5. Managing User Benefits

[1030] Users who agree to provide data will receive benefits such as discounts on communication charges and additional data volume depending on the amount of data provided. These benefits are calculated and managed by the server's benefit management means and reflected in the user's communication plan. Users can check the details of the benefits they have been granted through a dedicated application.

[1031] Specific examples

[1032] For example, consider the case where User A agrees to provide data. User A's device collects SMS data every day at 2:00 AM and removes phone numbers and names as part of personal information filtering. This filtered data is then converted into JSON format, compressed, and sent to the server. The server receives and stores this data, and uses it as training data for the AI ​​model. At the end of the month, the server calculates the amount of data provided by User A and grants discounts on communication charges and additional data volume. User A can check these benefits on the application.

[1033] The above is a specific embodiment of the present invention. This embodiment allows carriers to efficiently collect big data while providing substantial benefits to users. This system also meets the requirements for personal information protection, realizing a mechanism that benefits both users and carriers.

[1034] The processing flow will be explained below.

[1035] Step 1:

[1036] The user launches a dedicated application on the device and selects the option to consent to the provision of data. Once consent is confirmed, the device saves this setting in local storage and sends a consent notification to the server.

[1037] Step 2:

[1038] The device triggers a background task at the set interval (e.g., 2:00 AM every day) to begin data collection. The device retrieves and collects new messages from the SMS application.

[1039] Step 3:

[1040] The device scans the content of collected messages for personally identifiable information like phone numbers and names, and uses regular expressions to remove or mask this information to anonymize the data.

[1041] Step 4:

[1042] The device converts the anonymized data into JSON format and temporarily stores it locally, where it is then appropriately prepared as a file by the data filtering method.

[1043] Step 5:

[1044] The terminal compresses the filtered data using a method such as gzip and transmits it to the server using a secure communication protocol (e.g., HTTPS). The data transmission means manages this communication.

[1045] Step 6:

[1046] The server receives the HTTP request, decompresses the sent compressed data, verifies the data format and content for errors, and stores it in a local database.

[1047] Step 7:

[1048] The server analyzes the stored data. Using the data analysis means, the text data is processed with a natural language processing algorithm and necessary metadata (collection date and time, device ID, etc.) is added.

[1049] Step 8:

[1050] The server adds the analysis results to the training dataset of the generative AI model, which is then used to improve the accuracy of the generative AI model and develop new functions.

[1051] Step 9:

[1052] The server calculates the benefits for data-providing users and reflects them in the users' communication plans using a benefits management tool, often on a monthly basis.

[1053] Step 10:

[1054] The user checks the benefits through a dedicated application. The user opens the benefit confirmation screen on the application and obtains current benefit information from the server.

[1055] Step 11:

[1056] The device displays the benefit information it has obtained from the server on the user interface, allowing the user to check the benefits, such as discounts on communication charges and additional data volume.

[1057] Example 1

[1058] 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."

[1059] In modern information and communication technology, anonymization and efficient use of data collected from users are important challenges. There is also a need to improve the quantity and quality of collected data by offering appropriate rewards for the data provided by users. However, a system that efficiently anonymizes, compresses, securely transmits collected data, and then analyzes and manages rewards has yet to be fully developed. The present invention aims to solve these challenges and bring benefits to both users and service providers.

[1060] 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.

[1061] In this invention, the server includes a data collection means, a data filtering means, a data transmission means, a server receiving means, a data analysis means, and a reward management means. This allows data collected from user terminals to be converted into JSON format, compressed, and securely transmitted. Furthermore, by providing a means for setting consent to data provision via the user interface, a mechanism is provided for users to clearly consent to the provision of their own data, realizing a system that can provide appropriate rewards to users while thoroughly protecting personal information.

[1062] "Data collection means" refers to the functions and mechanisms for collecting data from user terminals.

[1063] "Data filtering measures" are functions or algorithms used to remove or mask personally identifiable information from collected data.

[1064] "Data transmission means" refers to the functions and protocols for transmitting anonymized data to a server.

[1065] "Server receiving means" refers to the function or mechanism by which the server receives and stores data.

[1066] "Data analysis means" refers to functions and algorithms for analyzing received data, which can be reflected in the generative AI model.

[1067] The "reward management means" refers to a function or module for providing users with rewards for providing data.

[1068] "Format conversion and compression means" refers to functions and algorithms for converting collected data into a specified format (e.g., JSON format) and compressing it.

[1069] "Consent setting means" refers to a function or interface that allows a user to set consent to the provision of data.

[1070] The present invention relates to a system for collecting data from user terminals, filtering, anonymizing, transmitting, receiving, analyzing, and managing benefits. Specific embodiments for carrying out the present invention will be described below.

[1071] 1. Overall system configuration

[1072] The system includes a user terminal, a server, and a user interface for users to check the rewards for providing data. The user terminal is equipped with a data collection means, a data filtering means, and a data transmission means. The server side includes a server reception means, a data analysis means, and a reward management means.

[1073] 2. Examples of Data Collection and Filtering

[1074] The user launches a dedicated application and agrees to provide data. Once consent is confirmed, the user's device periodically triggers data collection methods to collect data such as SMS and browsing history. For example, if a user configures SMS data collection at 2:00 AM every day, the data will be collected at the specified time. The collected data is anonymized by deleting or masking personal information. This anonymized data is then converted into JSON format.

[1075] 3. Examples of sending and receiving data

[1076] The anonymized data is compressed by the data transmission means and transmitted to the server using a secure communication protocol (such as HTTPS). The server receives the data via the server reception means, decompresses it, and stores it in a database.

[1077] 4. Analyzing the data and reflecting it in the generative AI model

[1078] The data stored on the server is analyzed by a data analysis means. The analyzed data is used as a training dataset for a generative AI model (e.g., ChatGPT). This training data improves the accuracy of the AI ​​model.

[1079] 5. Examples of User Benefit Management

[1080] Users who agree to provide data will receive benefits such as discounts on communication charges and additional data volume depending on the amount of data provided. These benefits are calculated and managed by the server's benefit management means and reflected in the user's communication plan. Users can check the details of the benefits they have been granted through a dedicated application.

[1081] Prompt Sentence Examples

[1082] Possible input prompts for a generative AI model include:

[1083] "Using the SMS data provided by users, please analyze their usage patterns."

[1084] "Guess your interests based on your browsing history."

[1085] By integrating the above functions, this system efficiently collects data from users, analyzes it, and uses it to train AI models. Furthermore, by offering appropriate benefits to users in exchange for providing their data, it has created a system that benefits both users and service providers.

[1086] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1087] Step 1:

[1088] The user launches the dedicated application and agrees to provide data.

[1089] What happens: The user opens the application, agrees to the terms and conditions for providing data, and then checks a specific box, after which their consent settings are saved within the app.

[1090] Input: User consent settings.

[1091] Output: The consent settings are saved and the next step is triggered.

[1092] Step 2:

[1093] The terminal periodically collects data using the data collection means.

[1094] Specific behavior: After consent is confirmed, the device's background service will be automatically triggered at a specific time (e.g., 2:00 AM) to collect necessary data, such as SMS data and browsing history.

[1095] Input: User consent settings and current time.

[1096] Output: Collected data (e.g. SMS, browsing history).

[1097] Step 3:

[1098] It filters the data collected by the device and anonymizes personal information.

[1099] What it does: Collected data is filtered to remove or mask any personally identifiable information, such as phone numbers and names, thereby anonymizing the data.

[1100] Input: Collected data.

[1101] Output: Anonymized data.

[1102] Step 4:

[1103] The device converts the anonymized data into JSON format, compresses it, and sends it to the server.

[1104] Specific operation: After the anonymized data is converted into JSON format, the data is compressed using a format such as ZIP and sent to the server using a secure communication protocol (e.g., HTTPS).

[1105] Input: Anonymized data.

[1106] Output: Compressed JSON data is sent to the server.

[1107] Step 5:

[1108] The server receives the data, unpacks it, and stores it in a database.

[1109] What happens: The server's API endpoint receives the data, unpacks it, and stores it in the database.

[1110] Input: Compressed JSON data.

[1111] Output: Data stored in a database.

[1112] Step 6:

[1113] The server analyzes the data using a data analysis means and reflects it in the training dataset of the generative AI model.

[1114] What it does: The stored data is analyzed and used as a training dataset for generative AI models (e.g., ChatGPT), using statistical methods and machine learning algorithms.

[1115] Input: Data stored in a database.

[1116] Output: Parsed data and a training dataset for a generative AI model.

[1117] Step 7:

[1118] The server calculates the amount of data provided by the user and grants rewards.

[1119] Specific operation: The server's reward management module aggregates the amount of data provided by each user and generates rewards such as discounts on communication charges or additional data volume based on the results.

[1120] Input: Parsed data and user-provided data volume.

[1121] Output: Benefit information (e.g., discounts on communication charges, additional data volume).

[1122] Step 8:

[1123] The user checks for offers through the application.

[1124] What it does: Users can open the "Rewards Information" section of the app and view details of the rewards they have been granted.

[1125] Input: Benefit information.

[1126] Output: Benefit details for user to review.

[1127] (Application example 1)

[1128] 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."

[1129] There is a need to improve production line efficiency by efficiently collecting and analyzing operational information, maintenance history, and defect information from robots used in factories. However, to effectively collect and analyze this data, it is essential to protect personal information and anonymize the data. It is also important to provide appropriate benefits to factory operators. This is expected to increase the efficiency of robot operations and improve the accuracy of predictive maintenance and defect detection.

[1130] 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.

[1131] In this invention, the server includes a data collection means for collecting data from user terminals, a data filtering means for anonymizing the data, a data transmission means for transmitting the anonymized data to the server, a server reception means for receiving and storing the data, a data analysis means for analyzing the data and reflecting it in a generative AI model, a reward management means for providing users with rewards for providing the data, and a factory robot having the function of collecting, anonymizing, compressing, and transmitting machine operation data, defect information, maintenance history, etc. This enables improved operational efficiency of the factory robot and improved accuracy of predictive maintenance and defect detection.

[1132] - "User terminal" means a device that has the ability to collect data and transmit it to a server.

[1133] "Data collection means" is a function for collecting necessary information from the terminal.

[1134] "Data filtering measures" are algorithms that remove or anonymize certain information from collected data.

[1135] The "data transmission means" is a function for transmitting anonymized data to a server.

[1136] The "server receiving means" is a function for receiving and storing transmitted data.

[1137] "Data analysis means" is a function for analyzing received data and reflecting it in the AI ​​model.

[1138] The "reward management means" is a function for calculating rewards for providing data and providing them to users.

[1139] A "factory robot" is a mechanical device that automates work within a factory and has the function of collecting operational data, maintenance history, and defect information.

[1140] A "generative AI model" is an artificial intelligence model that is trained based on analyzed data.

[1141] A "prompt" is an instruction or training example to be input into a generative AI model.

[1142] MODE FOR CARRYING OUT THE INVENTION

[1143] The present invention relates to a system that improves the efficiency of production lines by collecting and analyzing operation information, maintenance history, and defect information of robots used in factories. This system includes a user terminal, factory robots, a server, and an interface for privilege management.

[1144] Overall system configuration

[1145] The system of the present invention comprises the following components:

[1146] 1. User terminal: has data collection means, data filtering means, and data transmission means.

[1147] 2. Factory robots: They have the ability to collect machine operation data, malfunction information, maintenance history, etc., and transmit this information in an anonymized form.

[1148] 3. Server: Includes server receiving means, data analysis means, and benefit management means.

[1149] 4. Interface: Includes a means for users to check the rewards for the data they provide.

[1150] Data Collection and Filtering

[1151] The user (factory operator) launches a specific application on their device and agrees to provide data. Once this consent is confirmed, the factory robots will periodically collect data such as operation data, malfunction information, and maintenance history. The collected data is anonymized using data filtering methods.

[1152] Sending and Receiving Data

[1153] The anonymized data is compressed by the data transmission means and sent to the server. A secure protocol (e.g., SSL / TLS) is used for communication. The server receives the data via the server reception means, decompresses it, and stores it in a database.

[1154] Analyzing data and reflecting it in AI models

[1155] The data stored on the server is analyzed by a data analysis tool. The analyzed data is used as training data for a generative AI model (such as ChatGPT) and is used to improve the accuracy of the model.

[1156] Managing User Benefits

[1157] Users who agree to provide data will receive benefits according to the amount of data provided. These benefits include discounts on communication charges and additional data volume. These benefits are calculated and managed by a benefit management system and reflected in the user's communication plan. Users can check the details of the benefits they have received through a dedicated application.

[1158] Specific examples

[1159] For example, consider the case where a factory operator agrees to provide data. Factory robots collect operational data at a specific time each day and remove any personally identifiable information (such as robot ID and factory name). This filtered data is then converted into JSON format, compressed, and sent to a server. The server receives and stores this data, and uses it as training data for AI models. At the end of the month, the server calculates the amount of data provided by the user and awards them rewards. The user can view these rewards on the application.

[1160] Hardware and software used

[1161] Hardware: Factory robots, sensor devices

[1162] Software: Python, zlib library, requests library

[1163] Prompt Sentence Examples

[1164] "Design an application that collects operational data from factory robots, anonymizes it, and transmits it to a central server using a secure communication protocol. The central server analyzes the data and uses it as training data for a generative AI model."

[1165] This invention enables factory operators to carry out efficient operation and maintenance based on robot operational data, improving the accuracy of AI models.

[1166] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1167] Step 1:

[1168] The user device confirms consent to provide data. Once this consent is confirmed, the user device starts the process to collect the data. The input is the user's confirmed consent, and the output is the trigger for the data collection process. The specific behavior is that the user launches the application and clicks the consent button.

[1169] Step 2:

[1170] Factory robots collect operational data, malfunction information, maintenance history, etc. The collected data is sent as raw data to a user terminal. The inputs include the factory robot's operational data, malfunction records, and maintenance history, and the output is sent to the user terminal. Specifically, the data is collected using a sensor device and sent to the user terminal.

[1171] Step 3:

[1172] The collected data is filtered and anonymized on the user's device. Personally identifiable information (such as robot ID and factory name) is removed or masked. The input is the raw data sent by the factory robots, and the output is anonymized data. Specifically, an algorithm is run to remove identifying information.

[1173] Step 4:

[1174] The anonymized data is converted to JSON format and then compressed. This process converts the data into a transportable format. The input is the anonymized data, and the output is the data that has been converted to JSON format and compressed. The specific operations include converting the data to JSON format and then compressing it.

[1175] Step 5:

[1176] The compressed data is sent to the server using a secure communication protocol (e.g. SSL / TLS). The input is the compressed data, and the output is the securely transmitted data. The specific operation is to perform a request to send the data using the HTTPS protocol.

[1177] Step 6:

[1178] The server receives the transmitted data, decompresses it, and stores it in a database, which also serves as a repository for other data. The input is the compressed data, and the output is the decompressed data stored in the database. Specific operations include decompressing the data and writing it to the database.

[1179] Step 7:

[1180] The server analyzes the stored data and applies it to the generative AI model. The analyzed data is used as training data for the AI ​​model. The input is the raw data stored in the database, and the output is a trained AI model. Specific operations include applying a data analysis algorithm and training the AI ​​model.

[1181] Step 8:

[1182] The server grants rewards to users (factory operators) based on the amount of data provided. These rewards include discounts on communication charges and additional data volume. The input is the analyzed amount of data provided, and the output is the rewards granted. Specifically, the reward management means calculates the amount of data provided and applies the rewards to the user account.

[1183] Through these steps, the invention can efficiently and safely collect and analyze operational data from factory robots and use it to train AI models. Furthermore, by offering appropriate rewards to users for providing data, user satisfaction can be increased.

[1184] 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.

[1185] The present invention relates to a system that collects data from user terminals, anonymizes, transmits, receives, and analyzes the data, and further combines it with an emotion engine that recognizes the user's emotions. Specific embodiments for carrying out the present invention will be described below.

[1186] 1. Overall system configuration

[1187] The system of the present invention includes a user terminal, a server, and an interface for users to check rewards for providing data. The user terminal includes a data collection means, a data filtering means, a data transmission means, and an emotion engine. Meanwhile, the server includes a server reception means, a data analysis means, and a reward management means.

[1188] 2. Data Collection and Filtering

[1189] A user launches a specific application on their device and agrees to provide data. Once consent is confirmed, the user's device periodically triggers a data collection mechanism to collect data such as SMS and browsing history. The collected data is then anonymized by removing any personally identifiable information through a data filtering mechanism. This anonymized data is then converted into JSON format.

[1190] 3. Operation of the Emotion Engine

[1191] The anonymized data is then analyzed and labeled by a sentiment engine, which uses natural language processing algorithms to identify emotions within the text data and assigns corresponding labels (e.g., happy, sad, anger, etc.). This sentiment-labeled data is also stored in JSON format.

[1192] 4. Sending and Receiving Data

[1193] The anonymized data and emotion-labeled data are compressed by the data transmission means and transmitted to the server using a secure communication protocol. The server receives the data via the server reception means, decompresses it, and stores it in a database.

[1194] 5. Data analysis and reflection in AI models

[1195] The data stored on the server is analyzed by a data analysis tool. The analyzed data is used as a training dataset for a generative AI model (e.g., ChatGPT). Emotion-labeled data is also used as a training dataset, contributing to improving the accuracy of the AI ​​model.

[1196] 6. Managing User Benefits

[1197] Users who agree to provide data will receive benefits such as discounts on communication charges or additional data depending on the amount of data they provide. The benefit management method can also customize benefits based on emotion labels. For example, additional benefits can be provided to users with a high number of joy emotion labels.

[1198] 7. Check your benefits

[1199] Users can check their benefits through a dedicated application. When a user opens the benefit confirmation screen within the application, current benefit information is retrieved from the server and displayed.

[1200] Specific examples

[1201] For example, consider the case where User B agrees to provide data. User B's device collects SMS data every day at 2:00 AM and removes phone numbers and names as part of personal information filtering. This filtered data is then converted into JSON format, and an emotion engine assigns emotion labels to it. The emotion-labeled data is compressed and sent to the server, which receives and stores it. The server analyzes the received data and uses it as training data for the AI ​​model. At the end of the month, the server calculates discounts on communication charges and additional data volume based on User B's data provision volume and emotion labels, and grants benefits. User B can view these benefits on the application.

[1202] The above is a specific embodiment of the present invention, which allows carriers to efficiently collect big data while providing substantial benefits to users. The system satisfies the requirements for personal information protection and also improves the user experience by providing rewards that take user sentiment into account.

[1203] The processing flow will be explained below.

[1204] Step 1:

[1205] The user launches a dedicated application on the device and selects the option to consent to the provision of data. Once consent is confirmed, the device saves this setting in local storage and sends a consent notification to the server.

[1206] Step 2:

[1207] The device triggers a background task at a set interval (e.g., every day at 2:00 AM) to begin data collection. The device retrieves new data from SMS applications and browsing history.

[1208] Step 3:

[1209] The data collected by the device is processed using data filtering methods to detect personally identifiable information such as phone numbers, names, etc. This information is then removed or masked using regular expressions to anonymize the data.

[1210] Step 4:

[1211] The device converts the anonymized data into JSON format and temporarily stores it locally.

[1212] Step 5:

[1213] The device then runs an emotion engine that analyzes the anonymized data, using natural language processing algorithms to identify emotions within the text data and assigns them emotion labels such as "joy," "sadness," or "anger."

[1214] Step 6:

[1215] The emotion-labeled data is also stored in JSON format and compressed by the data transmission method.

[1216] Step 7:

[1217] The device sends the compressed data to the server using a secure communication protocol (e.g., HTTPS). An HTTP POST request is used for transmission.

[1218] Step 8:

[1219] The server receives the HTTP request, decompresses the compressed data, verifies the data format and content, and stores it in a local database.

[1220] Step 9:

[1221] The server analyzes the stored data using data analysis tools, and the analysis results are used as a training dataset for a generative AI model (e.g., ChatGPT).

[1222] Step 10:

[1223] The emotion-labeled data will also be analyzed and used as training data for the AI ​​model, which is expected to improve the accuracy of the AI ​​model's emotion recognition ability.

[1224] Step 11:

[1225] The server calculates rewards for data-providing users. Using reward management means, rewards are customized based on the amount of data provided by the user and their emotional labels. For example, users with a high number of "joy" emotional labels can be given additional rewards.

[1226] Step 12:

[1227] Users can check their benefits through a dedicated application. When a user opens the benefit confirmation screen within the application, current benefit information is retrieved from the server and displayed on the device screen.

[1228] Specific examples

[1229] For example, consider the case where User B agrees to provide data. User B's device collects SMS data every day at 2:00 AM, and personal information (phone number and name) is deleted using data filtering. The anonymized data is then converted into JSON format, and an emotion engine assigns an emotion label (e.g., "happy" or "sad"). This emotion-labeled data is compressed and sent to the server. The server receives the data, decompresses it, stores it, and analyzes it to use as training data for the AI ​​model. At the end of the month, the server calculates a discount on communication charges and additional data volume based on the amount of data provided by User B and the emotion label, and grants benefits. User B can view these benefits on the application.

[1230] In this way, the present invention realizes a series of processes: anonymizing, sentiment analysis, sending, receiving, and analyzing data collected from user devices, reflecting it in a generative AI model, and providing benefits to users. This provides a system that benefits both users and telecommunications carriers while thoroughly protecting personal information.

[1231] Example 2

[1232] 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."

[1233] In recent years, the spread of smart devices has led to a rapid increase in the amount of data generated by users. Systems that can effectively utilize this data and provide benefits to users are needed. However, an efficient system that consistently performs a series of processes, including data collection and anonymization, emotional labeling, secure data transmission, server analysis, and rewards management, has not yet been established. Furthermore, utilizing data while protecting user privacy is an important issue. Therefore, the present invention aims to provide a system that solves these problems.

[1234] The identification process by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes a data collection means for collecting data from user terminals, a data filtering means for anonymizing the data, a data transmission means for transmitting the anonymized data, a server reception means for receiving and storing the data, an emotion labeling means for assigning emotion labels to the anonymized data, a data analysis means for analyzing the labeled data and reflecting the results in a generative AI model, and a reward management means for providing users with rewards for providing the data. As a result, by consistently performing processes from data collection to analysis, emotion labeling, and reward management, it is possible to efficiently utilize user data and provide benefits to users.

[1235] "User terminal" means a device used by a user to generate and provide data, including a smartphone, tablet, or PC.

[1236] "Data Collection Measures" means functions or modules for collecting data generated by the User Device, which operate via specific applications or sensors.

[1237] "Data filtering measures" are functions or algorithms that remove or mask personally identifiable information from collected data.

[1238] "Data transmission means" refers to the functions and protocols for securely transmitting anonymized data to a server, and is often done via a communication line.

[1239] "Server receiving means" refers to a function or module for receiving data sent from a user terminal and saving it.

[1240] An "emotion labeling means" is a natural language processing algorithm or engine for assigning emotion labels to anonymized data.

[1241] "Data analysis means" refers to functions and modules that analyze received data and reflect it in the generative AI model.

[1242] The "reward management means" refers to a function or module for providing rewards to users in exchange for providing data, and is responsible for determining and notifying the content of the rewards.

[1243] A "generative AI model" is an artificial intelligence model that is trained using collected and analyzed data, and is used, for example, in natural language processing and sentiment analysis.

[1244] A "secure communication protocol" is a communication protocol that ensures security when sending and receiving data, and examples include HTTPS.

[1245] 1. Overall system configuration

[1246] The system of the present invention includes a user terminal, a server, and an interface for a user to check rewards for providing data. The user terminal includes a data collection means, a data filtering means, a data transmission means, and an emotion labeling means. Meanwhile, the server includes a server reception means, a data analysis means, and a reward management means.

[1247] 2. Data Collection and Filtering

[1248] Data collection is triggered when a user launches a specific application on their device and agrees to provide data. After confirming consent, the user's device periodically collects data such as SMS and browsing history. The collected data is anonymized by removing personally identifiable information using data filtering methods. This anonymized data is then converted to JSON format.

[1249] 3. Emotion Labeling

[1250] The anonymized data is then analyzed and labeled by a sentiment labeler, which uses natural language processing algorithms to identify emotions within the text data and assigns corresponding labels (e.g., happy, sad, anger, etc.). This sentiment-labeled data is also stored in JSON format.

[1251] 4. Data transmission

[1252] The anonymized data and emotion-labeled data are compressed by a data transmission means and transmitted to a server using a secure communication protocol. The device periodically transmits this data, allowing the server to store the latest data.

[1253] 5. Receiving and storing on the server

[1254] The server receives the data sent via the server receiving means, decompresses it, and stores it in a database. The server manages the received data and prepares it for analysis.

[1255] 6. Data analysis and reflection in AI models

[1256] The data analysis means on the server analyzes the stored data. The analyzed data is used as a training dataset for a generative AI model (e.g., ChatGPT). The emotion-labeled data is also used as a training dataset, contributing to improving the accuracy of the AI ​​model.

[1257] 7. Managing User Benefits

[1258] Users who agree to provide data can receive benefits based on the amount of data provided and the emotion label. The benefit management means manages these benefits and provides users with benefits such as discounts on communication charges or additional data.

[1259] 8. Check your benefits

[1260] Users can check their benefits through a dedicated application. When a user opens the benefit confirmation screen within the application, the latest benefit information is retrieved from the server and displayed.

[1261] Specific examples

[1262] For example, if a user agrees to provide data, the user's device will collect SMS data every day at 2:00 AM. Personally identifiable information (e.g., name, phone number) is removed from this data, anonymized, and converted into JSON format. An emotion labeling tool then assigns emotion labels such as "happy" or "sad." This labeled data is compressed and sent to a server via a secure communication protocol (e.g., HTTPS). The server receives the data, decompresses it, and stores it in a database. As a result of the analysis, new training data is added to the generative AI model, and the user can receive a discount on their communication charges at the end of the month, which can be confirmed in a dedicated app.

[1263] Prompt Sentence Examples

[1264] "SMS data collected from user devices is anonymized, emotion-labeled, converted to JSON format, and then sent to a server via a secure communication protocol. The received data is analyzed on the server and used as training data for the generative AI model. Rewards are then offered to users in the form of discounts on communication charges, which can then be viewed in the application."

[1265] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1266] Step 1: Start collecting data

[1267] The user launches the dedicated application on their device and agrees to provide data. This consent action initiates data collection. The input is the user's consent, and the output is the trigger for data collection. The specific action is when the user taps the "Data Provision Button."

[1268] Step 2: Collect data

[1269] The user device periodically collects data such as SMS and browsing history using a data collection method. The inputs are consent status and the specified data type, and the output is collected data. The specific operation is that the device scans and collects the target data at 2:00 AM every day.

[1270] Step 3: Filter and anonymize data

[1271] Personally identifiable information is removed from collected data using data filtering methods, resulting in anonymization. The input is collected raw data, and the output is anonymized data. Specifically, the process involves deleting the "Sato Taro" part from SMS data containing the name "Sato Taro" to anonymize it.

[1272] Step 4: Transforming the data and labeling it

[1273] The anonymized data is converted to JSON format, and then an emotion label is assigned to it by the emotion labeling means. The input is anonymized text data, and the output is JSON data with an emotion label assigned. The specific operation is to assign the label "joy" to the message "I'm so happy today!"

[1274] Step 5: Sending data

[1275] The emotion-labeled data is compressed and sent to the server by the data transmission means using a secure communication protocol. The input is emotion-labeled JSON data, and the output is a compressed data packet. The specific operation is to send the data to the server using HTTPS.

[1276] Step 6: Receiving and storing data

[1277] The server receives the data sent via the server receiving means, decompresses it, and stores it in the database. The input is the compressed data packet, and the output is the raw data stored in the database. The specific operation is to decompress the compressed data packet and insert it into the database.

[1278] Step 7: Analyze the data and incorporate it into the AI ​​model

[1279] The data analysis means in the server analyzes the stored data and reflects the results in the generative AI model. The input is the data to be analyzed, and the output is a training dataset for the generative AI model. The specific operation is to add emotion-labeled data to the dataset used for training.

[1280] Step 8: Manage and notify user benefits

[1281] The server calculates the rewards based on the amount of data provided by the user and the emotion label, and the reward management means notifies the user of the result. The input is the amount of data provided and the emotion label, and the output is the calculated reward information. The specific operation is to notify the user at the end of the month that "Your communication charges will be discounted by 20% depending on the amount of data provided."

[1282] Step 9: Check your benefits

[1283] The user checks the benefits through a dedicated application. The application retrieves the latest benefit information from the server and displays it. The input is a benefit confirmation request, and the output is the latest benefit information. The specific operation is for the user to select the "Benefits Confirmation" menu and check the current discount information and additional data volume.

[1284] (Application example 2)

[1285] 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."

[1286] Conventional advertising systems are unable to customize ads based on user emotions, making it difficult to deliver ads that effectively capture users' interests. Furthermore, from the perspective of protecting personal information, there was no system in place to safely manage collected data and provide users with special benefits.

[1287] The identification process by the identification 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 a data collection means for collecting data from user terminals, a data filtering means for anonymizing the data, and an emotion analysis means for assigning emotion labels to the anonymized data. This enables advertisement delivery based on user emotions.

[1288] A "user terminal" is a device such as a computer or mobile terminal used by a user.

[1289] A "data collection means" is a method or device for collecting data from a user terminal.

[1290] A "data filtering measure" is an algorithm or device used to remove or mask personally identifying information from collected data.

[1291] "Emotion analysis means" is a technology for identifying emotions in anonymized data and assigning emotion labels.

[1292] The "data transmission means" is a method or device for assigning emotion labels to anonymized data and transmitting it to a server.

[1293] The "server receiving means" is a method or device for receiving and storing data sent from a terminal.

[1294] "Data analysis means" refers to a method or device for analyzing received data and reflecting the results in the generative AI model.

[1295] The "reward management means" is a method or device for providing users with rewards for providing data and managing the rewards.

[1296] A "generative AI model" is an artificial intelligence model that is trained based on collected data.

[1297] "Emotion labels" are tags or marks that indicate a user's emotional state, obtained as a result of data analysis.

[1298] A "server" is a remote computing device that receives, analyzes, and stores data from multiple user terminals.

[1299] "Benefits" are rewards or benefits provided to users for actions such as providing data or viewing advertisements.

[1300] 1. Overall system configuration

[1301] This invention provides a system for realizing an emotion-intelligent advertising platform, including a user terminal, a server, and an interface for users to check rewards. The user terminal includes a data collection means, a data filtering means, an emotion analysis means, and a data transmission means. The server includes a server receiving means, a data analysis means, and a reward management means.

[1302] 2. Data Collection

[1303] When a user views an advertisement, the data collection means on the user's device uses the smartphone camera and built-in microphone to collect facial and voice data, which may reveal emotions such as interest, joy, indifference, and anger.

[1304] 3. Data Filtering

[1305] The collected data is anonymized using data filtering measures, such as face blurring, to remove any personally identifiable information, thereby protecting user privacy.

[1306] 4. Emotion analysis

[1307] The anonymized data is analyzed using natural language processing algorithms through sentiment analysis, for example using the Hugging Face transformers library to classify user emotions into labels such as "interested," "delighted," or "indifferent."

[1308] 5. Data Transmission

[1309] The emotion-labeled data is compressed by the data transmission means, encrypted using the HTTPS protocol, and sent to the server, ensuring data security.

[1310] 6. Data Reception and Analysis

[1311] The server receives the data sent via the server receiving means and stores it in a database. It then analyzes the data using the data analysis means as a training dataset for the generative AI model. In this process, it is possible to identify behavioral patterns and trends that are useful for ad delivery.

[1312] 7. Customizing and Offering Rewards

[1313] Users who consent to providing their data will be offered rewards using reward management tools. Rewards are customized based on emotional labels, so that, for example, users who are labeled as "interested" in an advertisement will receive additional rewards.

[1314] Specific examples

[1315] For example, let's say User A is viewing an advertisement on their smartphone. While viewing the advertisement, the smartphone's camera captures User A's facial expressions, and the built-in microphone collects their audio reactions. This data is blurred to anonymize personal information. Next, a natural language processing algorithm assigns an emotion label of "interest." The labeled data is securely sent to a server using HTTPS and analyzed there. The generated AI model uses this data to deliver new advertisements relevant to User A, thereby achieving more effective marketing. User A also earns points by viewing advertisements, which they can use to check for benefits such as discounts on communication charges within the app.

[1316] An example prompt might be something like, "Optimize the next ad shown to users based on the emotion label (e.g., interest, delight, indifference) they received when viewing a specific product ad."

[1317] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1318] Step 1:

[1319] The user device collects facial expression and voice data from the user while viewing the advertisement. Data is input using the camera and microphone of the smartphone or head-mounted display. This collected data becomes the basis for reflecting the user's emotions.

[1320] Step 2:

[1321] The collected data is then processed on the device using data filtering to remove personal information. Specifically, OpenCV is used to blur faces and anonymize the data. This process converts the raw data into anonymous data with personal information removed.

[1322] Step 3:

[1323] The anonymized data is then analyzed by a sentiment analysis tool, which uses Hugging Face's transformers library to run natural language processing algorithms. Based on the input anonymous data, the user's emotions are labeled as "interest," "happiness," "indifference," etc., generating emotion-labeled data. This data can then be used to deliver advertisements.

[1324] Step 4:

[1325] The emotion-labeled data is compressed by the data transmission means and sent to the server using the HTTPS protocol. The data is protected by a secure communication means and is therefore transported safely. In this step, the emotion-labeled data is transferred to the server.

[1326] Step 5:

[1327] The server receives the data sent from the terminal using the server receiving means. The received data is temporarily saved and stored in a database. The saved data is ready for analysis.

[1328] Step 6:

[1329] The data stored on the server undergoes detailed analysis by a data analysis tool. The analysis results are used as a training dataset for the generative AI model. The accuracy of the AI ​​model is improved based on the input emotion-labeled data. The results of the analysis are reflected in the ad distribution system.

[1330] Step 7:

[1331] The server uses a reward management means to provide rewards to users who agree to provide data. The rewards are customized based on the emotion label. For example, if a user is labeled as "interested," an additional reward is provided. The output data with the rewards is distributed to the user as a final notification, which the user can view in a dedicated application.

[1332] 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.

[1333] 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.

[1334] 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.

[1335] 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.

[1336] 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.

[1337] 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.

[1338] 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).

[1339] 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.

[1340] 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."

[1341] 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.

[1342] 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).

[1343] 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.

[1344] 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.

[1345] 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.

[1346] 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.

[1347] 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.

[1348] 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.

[1349] 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.

[1350] 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.

[1351] 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.

[1352] 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.

[1353] The following is further disclosed regarding the above embodiment.

[1354] (Claim 1)

[1355] a data collection means for collecting data from a user terminal;

[1356] data filtering means for anonymizing said data;

[1357] a data transmission means for transmitting the anonymized data to a server;

[1358] a server receiving means for receiving and storing the data;

[1359] A data analysis means for analyzing the data and reflecting the data in a generative AI model;

[1360] a reward management means for providing a reward to a user for providing said data;

[1361] A system including:

[1362] (Claim 2)

[1363] 10. The system of claim 1, wherein the data collection means is configured to collect data periodically with the user's consent.

[1364] (Claim 3)

[1365] 10. The system of claim 1, wherein the data filtering means uses an algorithm to remove or mask personally identifying information from the collected data.

[1366] (Claim 4)

[1367] 2. The system according to claim 1, wherein the data transmission means compresses the data and transmits it to the server using a secure communication protocol.

[1368] (Claim 5)

[1369] 2. The system according to claim 1, wherein the reward management means is configured to provide the user with a discount on communication charges or additional data volume depending on the amount of collected data.

[1370] "Example 1"

[1371] (Claim 1)

[1372] a data collection means for collecting data from a user terminal;

[1373] data filtering means for anonymizing said data;

[1374] a data transmission means for transmitting the anonymized data to a server;

[1375] a server receiving means for receiving and storing the data;

[1376] A data analysis means for analyzing the data and reflecting the data in a generative AI model;

[1377] a reward management means for providing a reward to a user for providing said data;

[1378] A format conversion and compression means for converting the data into a JSON format, compressing it, and transmitting it;

[1379] The system is characterized in that it has a consent setting means for consenting to the provision of data via a user interface.

[1380] A system including:

[1381] (Claim 2)

[1382] 10. The system of claim 1, wherein the data collection means is configured to collect data periodically with the user's consent.

[1383] (Claim 3)

[1384] 10. The system of claim 1, wherein the data filtering means uses an algorithm to remove or mask personally identifying information from the collected data.

[1385] "Application Example 1"

[1386] (Claim 1)

[1387] a data collection means for collecting data from a user terminal;

[1388] data filtering means for anonymizing said data;

[1389] a data transmission means for transmitting the anonymized data to a server;

[1390] a server receiving means for receiving and storing the data;

[1391] A data analysis means for analyzing the data and reflecting the data in a generative AI model;

[1392] a reward management means for providing a reward to a user for providing said data;

[1393] A system that includes factory robots with the ability to collect machine operation data, malfunction information, maintenance history, etc., and transmit them after anonymizing and compressing them.

[1394] (Claim 2)

[1395] 10. The system of claim 1, wherein the data collection means is configured to collect data periodically with the user's consent.

[1396] (Claim 3)

[1397] 10. The system of claim 1, wherein the data filtering means uses an algorithm to remove or mask personally identifying information from the collected data.

[1398] (Claim 4)

[1399] 10. The system of claim 1, wherein the data collected from the factory robots is used to provide incentives to factory operators.

[1400] (Claim 5)

[1401] 2. The system of claim 1, wherein the analyzed data is used as training data for a generative AI model to generate prompt sentences.

[1402] "Example 2: Combining Emotion Engines"

[1403] (Claim 1)

[1404] a data collection means for collecting data from a user terminal;

[1405] data filtering means for anonymizing said data;

[1406] a data transmission means for transmitting the anonymized data to a server;

[1407] a server receiving means for receiving and storing the data;

[1408] an emotion labeling means for assigning emotion labels to the anonymized data;

[1409] a data analysis means for analyzing the labeled data and reflecting the results in a generative AI model;

[1410] a reward management means for providing a reward to a user for providing said data;

[1411] A system including:

[1412] (Claim 2)

[1413] 10. The system of claim 1, wherein the data collection means is configured to collect data periodically with the user's consent.

[1414] (Claim 3)

[1415] 10. The system of claim 1, wherein the data filtering means uses an algorithm to remove or mask personally identifying information from the collected data.

[1416] "Application example 2 when combining emotion engines"

[1417] (Claim 1)

[1418] a data collection means for collecting data from a user terminal;

[1419] data filtering means for anonymizing said data;

[1420] sentiment analysis means for assigning sentiment labels to the anonymized data;

[1421] a data transmission means for transmitting the emotion-labeled data to a server;

[1422] a server receiving means for receiving and storing the data;

[1423] A data analysis means for analyzing the data and reflecting the data in a generative AI model;

[1424] reward management means for customizing rewards based on the emotion labels and providing the rewards to the users;

[1425] A system including:

[1426] (Claim 2)

[1427] 10. The system of claim 1, wherein the data collection means is configured to collect data periodically with the user's consent.

[1428] (Claim 3)

[1429] 10. The system of claim 1, wherein the data filtering means uses an algorithm to remove or mask personally identifying information from the collected data. [Explanation of symbols]

[1430] 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. a data collection means for collecting data from a user terminal; data filtering means for anonymizing said data; a data transmission means for transmitting the anonymized data to a server; a server receiving means for receiving and storing the data; A data analysis means for analyzing the data and reflecting the data in a generative AI model; a reward management means for providing a reward to a user for providing said data; A system including:

2. 2. The system of claim 1, wherein the data collection means is configured to collect data periodically based on the user's consent.

3. 10. The system of claim 1, wherein the data filtering means uses an algorithm to remove or mask personally identifying information from the collected data.

4. 2. The system of claim 1, wherein the data transmission means compresses the data and transmits it to the server using a secure communication protocol.

5. 2. The system according to claim 1, wherein the benefit management means is configured to provide a user with a discount on communication charges or an additional amount of data depending on the amount of collected data.

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