system

Abstract

The system according to this embodiment aims to securely manage user data and provide appropriate compensation. [Solution] The system according to the embodiment comprises a collection unit, a data protection unit, a data provision unit, a market research unit, a product development unit, and a reward provision unit. The collection unit collects data such as purchase history, location information, and health data from users. The data protection unit securely manages the data collected by the collection unit. The data provision unit provides the data managed by the data protection unit to companies. The market research unit conducts market research and product development using the data provided by the data provision unit. The product development unit improves product development and marketing strategies based on the results obtained by the market research unit. The reward provision unit provides rewards to users based on the data provided by the data provision unit.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, there is a problem that a mechanism for safely managing user data while providing appropriate rewards is not sufficiently established.

[0005] The system according to the embodiment aims to safely manage user data and provide appropriate rewards.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a data collection unit, a data protection unit, a data provision unit, a market research unit, a product development unit, and a reward provision unit. The data collection unit collects data from users, such as purchase history, location information, and health data. The data protection unit securely manages the data collected by the data collection unit. The data provision unit provides the data managed by the data protection unit to companies. The market research unit conducts market research and product development using the data provided by the data provision unit. The product development unit improves product development and marketing strategies based on the results obtained by the market research unit. The reward provision unit provides rewards to users based on the data provided by the data provision unit. [Effects of the Invention]

[0007] The system according to this embodiment can securely manage user data and provide appropriate rewards. [Brief explanation of the drawing]

[0008] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Modes for carrying out the invention]

[0009] Hereinafter, an example of an embodiment of the system relating to the technology of this disclosure will be described with reference to the attached drawings.

[0010] First, let's explain the terminology used in the following explanation.

[0011] In the following embodiments, the signed processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Furthermore, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include CPU (Central Processing Unit), GPU (Graphics Processing Unit), GPGPU (General-Purpose computing on Graphics Processing Units), APU (Accelerated Processing Unit), or TPU (Tensor Processing Unit).

[0012] In the following embodiments, signed RAM (Random Access Memory) is a memory that temporarily stores information and is used as work memory by the processor.

[0013] In the following embodiments, the signed storage is one or more non-volatile storage devices that store various programs and various parameters. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes.

[0014] In the following embodiments, the labeled communication I / F (Interface) is an interface including a communication processor, an antenna, and the like. The communication I / F controls communication between a plurality of computers. Examples of communication standards applicable to the communication I / F include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B". That is, "A and / or B" means that it may be only A, only B, or a combination of A and B. Also, in this specification, when expressing three or more matters connected by "and / or", the same concept as "A and / or B" is applied.

[0016] [First Embodiment] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0017] As shown in FIG. 1, the data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0018] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. Also, the database 24 and the communication I / F 26 are connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0019] The smart device 14 comprises a computer 36, a receiving device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The receiving device 38, output device 40, and camera 42 are also connected to the bus 52.

[0020] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, and accepts user input. The touch panel 38A accepts user input via touch by detecting contact with an object (e.g., a pen or finger). The microphone 38B accepts user input via voice by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 (see Figure 2) acquires the data indicating the user input.

[0021] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user by outputting the data in a form perceptible to the user (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

[0023] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0024] As shown in Figure 2, in the data processing device 12, a specific processing is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" related to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0025] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0026] In the smart device 14, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used in conjunction with the specific processing program 56 by the data processing system 10. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart device 14 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0027] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device (e.g., a generation server) may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device having the data generation model 58. The data processing device 12 may also be a server device or a terminal device owned by a user (e.g., a mobile phone, robot, home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example of form 1) The data provision system according to an embodiment of the present invention is a system in which individuals can directly provide their first-party data to companies and receive appropriate compensation for the use of that data. The data provision system allows users to securely share purchase history, location information, health data, etc., through an application, and accumulate points each time the data is used. Companies can utilize this real-time data for market research, product development, and personalized marketing. Furthermore, companies can have individuals answer detailed questionnaires about their lifestyle and consumption behavior, allowing them to understand consumer needs and problems and use this information to improve product development and marketing strategies. For example, the data provision system allows users to provide their data through an application, such as purchase history, location information, and health data. This data is securely shared, and users earn points each time the data is used. These points are received by users as compensation and can be used, for example, as points in an electronic payment system. Next, the data provision system allows companies to utilize the data provided by users in real time. For example, it can be used for market research, product development, and personalized marketing. Based on users' purchase history and location information, companies can analyze consumer behavior patterns and formulate optimal marketing strategies. Furthermore, companies conduct detailed surveys of users regarding their lifestyles and consumption behavior to understand consumer needs and problems. They also use the data obtained from users to improve product development and marketing strategies. For example, they analyze users' purchase history to identify products and services that consumers desire. They also utilize users' location information to understand regional consumption behavior and develop region-specific marketing strategies. This allows data provision systems to enable users to control their own data while receiving appropriate compensation. Companies can also efficiently acquire high-quality first-party data and use it to improve the quality of their products and services. This results in a system that benefits both individuals and businesses.This allows the data provision system to securely manage user data and provide it to companies, which can then use it for market research and product development, while users can earn rewards.

[0029] The data provision system according to this embodiment comprises a collection unit, a data protection unit, a data provision unit, a market research unit, a product development unit, and a reward provision unit. The collection unit collects data from users, such as purchase history, location information, and health data. For example, the collection unit collects the user's purchase history. Purchase history includes the type of product purchased, the date and time of purchase, and the location of purchase. For example, the collection unit collects the user's location information. Location information includes GPS data, Wi-Fi location information, beacon data, etc. The collection unit collects the user's health data. Health data includes heart rate, steps taken, sleep data, etc. The data protection unit securely manages the data collected by the collection unit. For example, the data protection unit encrypts the collected data for secure management. Algorithms such as AES, RSA, and SHA are used for encryption. For example, the data protection unit performs access control to the collected data. Access control includes user authentication and authorization management. For example, the data protection unit backs up the collected data. Backups include regular data copying and recovery processes. The Data Provision Department provides data managed by the Data Protection Department to companies. The Data Provision Department provides data to companies in real time, for example. Real time includes data delivery delay time and update frequency. The Data Provision Department provides data to companies in specified formats, for example. Data formats include CSV, JSON, XML, etc. The Data Provision Department sets the data delivery frequency for companies, for example. Delivery frequency includes real time, periodic, on-demand, etc. The Market Research Department uses the data provided by the Data Provision Department to conduct market research and product development. The Market Research Department analyzes consumer behavior patterns based on the provided data, for example. Analysis of behavior patterns includes clustering, time series analysis, and behavioral forecasting. The Market Research Department understands consumer needs based on the provided data, for example. Understanding consumer needs includes surveys, data analysis, and consumer interviews. The Market Research Department identifies consumer problems based on the provided data, for example.Identifying consumer issues includes data mining, text mining, and feedback analysis. The product development department improves product development and marketing strategies based on the results obtained by the market research department. For example, the product development department develops products based on the analysis results. Product development includes prototyping, user testing, and feedback collection. The product development department improves marketing strategies based on the analysis results. Improving marketing strategies includes A / B testing, user feedback, and performance evaluation. The product development department develops new products based on the analysis results. New product development includes concept design, prototyping, and mass production. The rewards department provides rewards to users based on the data provided by the data provision department. For example, the rewards department provides points to users each time data is used. Points include criteria for awarding points, usage methods, and exchangeable items. The rewards department provides coupons to users. Coupons include discount coupons, free coupons, and special offer coupons. The rewards department provides cash to users. Cash includes bank transfers, electronic money, and gift cards. As a result, the data provision system according to this embodiment securely manages user data and provides it to companies, allowing companies to utilize it for market research and product development, while users can earn rewards.

[0030] The data collection unit collects data from users, including purchase history, location information, and health data. Specifically, when collecting a user's purchase history, detailed information such as the type of product purchased, the date and time of purchase, and the location of purchase is included. This allows for an understanding of the user's purchasing trends and preferences. Location information collection includes GPS data, Wi-Fi location information, and beacon data, allowing for the identification of the user's movement patterns and visited locations. For example, if a user frequently visits a particular store, it becomes possible to provide promotional information for that store. Health data collection includes heart rate, step count, and sleep data, allowing for an understanding of the user's health status and lifestyle habits. This enables the suggestion of health-related products and services. The data collection unit centrally manages this data and updates it in real time, ensuring that users always have access to the latest information. Furthermore, the data collection unit can flexibly adjust the data collection method and frequency, efficiently collecting necessary data while respecting user privacy. For example, if a user refuses to provide certain data, the system can be configured not to collect that data. This allows the data collection unit to effectively collect necessary data while gaining the user's trust.

[0031] The Data Protection Department securely manages the data collected by the Collection Department. Specifically, it encrypts the collected data for secure management. Advanced encryption algorithms such as AES, RSA, and SHA are used for encryption to prevent unauthorized access and leakage of data. The Data Protection Department implements access control to the collected data, strictly restricting access to the data through user authentication and permission management. For example, it can be configured so that only users with specific permissions can access specific data. Furthermore, the Data Protection Department regularly backs up the collected data to prepare for data loss or corruption. Backups involve storing copies of the data in multiple locations and establishing a recovery process to ensure a quick response in the event of an emergency. The Data Protection Department stays informed about the latest technologies and regulations regarding data protection and continuously improves the security of the system. For example, introducing new encryption technologies and security protocols can further enhance data security. This allows the Data Protection Department to securely manage user data and realize a reliable data delivery system.

[0032] The Data Provision Department provides companies with data managed by the Data Protection Department. Specifically, it provides companies with real-time data. Real-time data provision minimizes data delivery delays and increases update frequency, enabling companies to quickly utilize the latest data. The Data Provision Department provides companies with data in specified formats. These formats include CSV, JSON, XML, etc., allowing data to be provided in a format suitable for the company's systems and applications. Furthermore, the Data Provision Department sets the data provision frequency for companies. This frequency includes real-time, periodic, and on-demand options, enabling flexible data provision tailored to the company's needs. For example, data can be provided in real-time during specific campaign periods and updated regularly during normal times. The Data Provision Department strengthens collaboration with companies and builds a feedback loop to maintain data quality and accuracy. This allows companies to conduct market research and product development based on the provided data, and the Data Provision Department to improve data provision based on feedback from companies. In this way, the Data Provision Department can provide companies with high-quality data and support their business growth.

[0033] The Market Research Department conducts market research and product development using data provided by the Data Provision Department. Specifically, it analyzes consumer behavior patterns based on the provided data. Advanced data analysis techniques such as clustering, time series analysis, and behavioral prediction are used to analyze behavioral patterns, allowing for a detailed understanding of consumer purchasing trends and preferences. The Market Research Department also understands consumer needs based on the provided data. This includes surveys, data analysis, and consumer interviews, enabling the identification of products and services that consumers desire. Furthermore, the Market Research Department identifies consumer problems based on the provided data. Data mining, text mining, and feedback analysis are used to identify consumer problems, revealing the challenges and frustrations that consumers face. Based on these analysis results, the Market Research Department provides companies with concrete improvement suggestions and new product development ideas. In this way, the Market Research Department contributes to improving companies' product development and marketing strategies, and enhances consumer satisfaction.

[0034] The Product Development Department improves product development and marketing strategies based on the results obtained by the Market Research Department. Specifically, it develops products based on the analysis results. Product development includes prototyping, user testing, and feedback collection, enabling the rapid development of products that meet consumer needs. The Product Development Department also improves marketing strategies based on the analysis results. This includes A / B testing, user feedback, and performance evaluation, enabling the deployment of effective promotions and advertising campaigns. Furthermore, the Product Development Department develops new products based on the analysis results. New product development includes concept design, prototyping, and mass production, enabling the introduction of innovative products that meet consumer expectations. By strengthening collaboration with the Market Research Department and continuously collecting consumer feedback, the Product Development Department can improve the quality of products and services. This allows the Product Development Department to enhance the company's competitiveness and achieve sustainable growth.

[0035] The rewards department provides rewards to users based on the data provided by the data department. Specifically, users receive points each time their data is used. Points include criteria for awarding points, usage methods, and exchangeable items, allowing users to accumulate points and exchange them for various benefits. The rewards department also provides coupons to users. These coupons include discount coupons, free coupons, and special offer coupons, allowing users to use products and services at a discount. Furthermore, the rewards department provides cash to users. This cash can be received via bank transfer, electronic money, gift cards, etc., allowing users to receive rewards according to their preferences. The rewards department can continuously review and improve the types and methods of rewards to enhance user satisfaction. For example, introducing new reward programs or reviewing reward awarding criteria can increase user participation. This allows the rewards department to strengthen user incentives for data provision and improve the overall data collection efficiency of the system.

[0036] The data collection unit can collect user purchase history, location information, health data, etc. For example, the data collection unit can collect user purchase history. Purchase history includes the type of product purchased, the date and time of purchase, and the location of purchase. For example, the data collection unit can collect user location information. Location information includes GPS data, Wi-Fi location information, beacon data, etc. For example, the data collection unit can collect user health data. Health data includes heart rate, steps taken, sleep data, etc. By collecting user purchase history, location information, health data, etc., companies can obtain detailed data. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input user purchase history data into a generating AI and have the generating AI perform analysis of the purchase history.

[0037] The data protection unit can encrypt and securely manage the collected data. For example, the data protection unit encrypts and securely manages the collected data. Algorithms such as AES, RSA, and SHA are used for encryption. For example, the data protection unit performs access control to the collected data. Access control includes user authentication and permission management. For example, the data protection unit backs up the collected data. Backups include periodic data copying and recovery processes. By encrypting and securely managing the collected data, data leakage can be prevented. Some or all of the above processes in the data protection unit may be performed using AI, for example, or without AI. For example, the data protection unit can input collected data into a generating AI and have the generating AI perform data encryption.

[0038] The data provision department can provide data to companies in real time. For example, the data provision department can provide data to companies in real time. Real time includes data provision delay time, update frequency, etc. The data provision department can provide data to companies in a specified format. Data formats include CSV, JSON, XML, etc. The data provision department can set the frequency of data provision to companies. Provision frequency includes real time, periodic, on-demand, etc. By providing data to companies in real time, companies can utilize the latest data. Some or all of the above processing in the data provision department may be performed using AI, for example, or not using AI. For example, the data provision department can input the data to be provided to companies into a generating AI and have the generating AI perform the data provision.

[0039] The market research department can analyze consumer behavior patterns based on the provided data. For example, the market research department can analyze consumer behavior patterns based on the provided data. Analysis of behavior patterns includes clustering, time series analysis, and behavioral forecasting. For example, the market research department can understand consumer needs based on the provided data. Understanding consumer needs includes surveys, data analysis, and consumer interviews. For example, the market research department can identify consumer problems based on the provided data. Identifying consumer problems includes data mining, text mining, and feedback analysis. In this way, by analyzing consumer behavior patterns based on the provided data, companies can understand consumer needs. Some or all of the above processes in the market research department may be performed using AI, for example, or not using AI. For example, the market research department can input the provided data into a generating AI and have the generating AI perform an analysis of consumer behavior patterns.

[0040] The product development department can improve product development and marketing strategies based on the analysis results. For example, the product development department can develop products based on the analysis results. Product development includes prototyping, user testing, and feedback collection. For example, the product development department can improve marketing strategies based on the analysis results. Improvement of marketing strategies includes A / B testing, user feedback, and performance evaluation. For example, the product development department can develop new products based on the analysis results. New product development includes concept design, prototyping, and mass production. By improving product development and marketing strategies based on analysis results, companies can formulate more effective products and strategies. Some or all of the above processes in the product development department may be performed using AI, for example, or not. For example, the product development department can input analysis results into a generating AI and have the generating AI perform improvements to product development and marketing strategies.

[0041] The rewards department can provide users with points each time their data is used. For example, the rewards department can provide users with points each time their data is used. Points include criteria for awarding points, how to use them, and items they can be exchanged for. The rewards department can provide users with coupons, for example. Coupons include discount coupons, free coupons, and special offer coupons. The rewards department can provide users with cash, for example. Cash includes bank transfers, electronic money, and gift cards. This way, by providing users with points each time their data is used, users can have an incentive to provide data. Some or all of the above processing in the rewards department may be performed using AI, for example, or not using AI. For example, the rewards department can input data usage information into a generating AI and have the generating AI award points.

[0042] The market research department can conduct surveys with users regarding their lifestyles and consumer behavior. For example, the market research department might conduct surveys with users regarding their lifestyles and consumer behavior. These surveys might include questions about daily activities, consumer behavior, hobbies, and preferences. Based on the survey results, the market research department might understand consumer needs. This understanding could include statistical analysis, text mining, and cross-tabulation. Based on the survey results, the market research department might identify consumer problems. This identification could include data mining, text mining, and feedback analysis. This allows companies to understand the detailed needs of consumers by conducting surveys with users regarding their lifestyles and consumer behavior. Some or all of the above processes in the market research department may be performed using AI, or not. For example, the market research department could input survey results into a generating AI and have the generating AI perform the task of understanding consumer needs.

[0043] The product development department can improve product development and marketing strategies based on survey results. For example, the product development department can develop products based on survey results. Product development includes prototyping, user testing, and feedback collection. The product development department can improve marketing strategies based on survey results. Improvement of marketing strategies includes A / B testing, user feedback, and performance evaluation. The product development department can develop new products based on survey results. New product development includes concept design, prototyping, and mass production. By improving product development and marketing strategies based on survey results, companies can formulate products and strategies that meet consumer needs. Some or all of the above processes in the product development department may be performed using AI, for example, or not. For example, the product development department can input survey results into a generating AI and have the generating AI perform improvements to product development and marketing strategies.

[0044] The data collection unit can analyze the user's past data provision history and select the optimal data collection method. For example, the data collection unit may prioritize collecting the types of data that the user has frequently provided in the past. For example, the data collection unit may prioritize suggesting data collection methods that the user has used in the past (manual, sensor, etc.). For example, the data collection unit may select the optimal data collection method for a specific time period based on the user's past data provision history. This allows for efficient data collection by analyzing the user's past data provision history and selecting the optimal data collection method. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's past data provision history into a generating AI and have the generating AI select the optimal data collection method.

[0045] The data collection unit can filter data based on the user's current activities and areas of interest during data collection. For example, if the user is exercising, the data collection unit will prioritize collecting health data. For example, if the user is shopping, the data collection unit will prioritize collecting purchase history data. For example, if the user is traveling, the data collection unit will prioritize collecting location information data. By filtering the data based on the user's current activities and areas of interest, it is possible to collect highly relevant data. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's current activities and areas of interest into a generating AI and have the generating AI perform data filtering.

[0046] The data collection unit can prioritize the collection of highly relevant data based on the user's geographical location information during data collection. For example, if the user is in a specific region, the data collection unit will prioritize the collection of purchase history data related to that region. For example, if the user is traveling, the data collection unit will prioritize the collection of location information data related to the travel destination. For example, if the user is participating in a specific event, the data collection unit will prioritize the collection of data related to that event. This allows for the efficient collection of region-specific data by prioritizing the collection of highly relevant data based on the user's geographical location information. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's geographical location information into a generating AI and have the generating AI perform the collection of highly relevant data.

[0047] The data collection unit can analyze a user's social media activity and collect relevant data during data collection. For example, the data collection unit can collect relevant purchase history data based on information shared by the user on social media. For example, the data collection unit can collect location data of places checked in by the user on social media. For example, the data collection unit can collect relevant health data based on health information mentioned by the user on social media. This allows for the efficient collection of relevant data by analyzing the user's social media activity. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's social media activity data into a generating AI and have the generating AI perform the collection of relevant data.

[0048] The data protection unit can adjust the encryption strength based on the importance of the collected data during data protection. For example, the data protection unit applies strong encryption to important data such as health data. For example, it applies standard encryption to common data such as purchase history data. For example, it applies light encryption to temporary data such as location data. This optimizes the level of data protection by adjusting the encryption strength based on the importance of the collected data. Some or all of the above processing in the data protection unit may be performed using AI, for example, or without AI. For example, the data protection unit can input the importance of the collected data into a generating AI and have the generating AI adjust the encryption strength.

[0049] The data protection unit can apply different protection algorithms depending on the data category during data protection. For example, the data protection unit can apply an advanced medical protection algorithm to health data. For example, it can apply a standard commercial protection algorithm to purchase history data. For example, it can apply a location-specific protection algorithm to location data. By applying different protection algorithms depending on the data category, the accuracy of data protection can be improved. Some or all of the above processing in the data protection unit may be performed using AI, for example, or without AI. For example, the data protection unit can input the data category into a generating AI and have the generating AI execute the application of the protection algorithm.

[0050] The data protection unit can determine the priority of protection based on when the data was submitted. For example, the data protection unit may prioritize the protection of recently collected data. For example, it may apply normal protection to data collected in the past. For example, it may apply protection appropriate to data collected during a specific period. This allows for the priority of protecting the most recent data by determining the priority of protection based on when the data was submitted. Some or all of the above processing in the data protection unit may be performed using AI, for example, or without AI. For example, the data protection unit may input the data submission date into a generating AI and have the generating AI perform the determination of the protection priority.

[0051] The data protection unit can adjust the order of protection based on the relevance of the data during data protection. For example, the data protection unit may prioritize the protection of important data. For example, it may perform normal protection on general data. For example, it may perform light protection on temporary data. This allows for priority protection of important data by adjusting the order of protection based on the relevance of the data. Some or all of the above processing in the data protection unit may be performed using AI, for example, or without AI. For example, the data protection unit may input the relevance of the data into a generating AI and have the generating AI perform the adjustment of the order of protection.

[0052] The data provision unit can adjust the level of detail of the data provided based on the company's needs. For example, if a company requires detailed data, the data provision unit will provide detailed data. For example, if a company requires general data, the data provision unit will provide standard data. For example, if a company requires simplified data, the data provision unit will provide simplified data. In this way, by adjusting the level of detail of the data provided based on the company's needs, the data provision unit can provide the most suitable data for the company. Some or all of the above processing in the data provision unit may be performed using AI, for example, or without AI. For example, the data provision unit can input the company's needs into a generating AI and have the generating AI perform the adjustment of the level of detail of the data.

[0053] The data provision unit can apply different provision algorithms depending on the industry of the company when providing data. For example, the data provision unit can apply a provision algorithm specifically for medical data to the medical industry. For example, the data provision unit can apply a provision algorithm specifically for purchase history data to the retail industry. For example, the data provision unit can apply a provision algorithm specifically for location information data to the travel industry. By applying different provision algorithms depending on the industry of the company, it becomes possible to provide industry-specific data. Some or all of the above processing in the data provision unit may be performed using AI, for example, or without using AI. For example, the data provision unit can input the industry of the company into a generating AI and have the generating AI execute the application of the provision algorithm.

[0054] The data provision unit can prioritize providing highly relevant data based on a company's geographical location information when providing data. For example, if a company needs data related to a specific region, the data provision unit will prioritize providing data for that region. For example, if a company needs global data, the data provision unit will provide broad-ranging data. For example, if a company needs data related to a specific city, the data provision unit will prioritize providing data for that city. This allows for the efficient provision of region-specific data by prioritizing the provision of highly relevant data based on a company's geographical location information. Some or all of the above processing in the data provision unit may be performed using AI, for example, or without AI. For example, the data provision unit can input a company's geographical location information into a generating AI and have the generating AI perform the task of providing highly relevant data.

[0055] The data provision unit can analyze a company's social media activities and provide relevant data when providing data. For example, the data provision unit can provide data related to topics mentioned by the company on social media. For example, the data provision unit can provide data related to campaigns conducted by the company on social media. For example, the data provision unit can provide relevant data based on information shared by the company on social media. This allows for the efficient provision of relevant data by analyzing a company's social media activities. Some or all of the above processing in the data provision unit may be performed using AI, for example, or without AI. For example, the data provision unit can input a company's social media activity data into a generating AI and have the generating AI perform the provision of relevant data.

[0056] The market research department can improve the accuracy of its research by considering the interrelationships between data. For example, the market research department can analyze the interrelationships between purchase history data and location data to clarify consumer behavior patterns. For example, the market research department can analyze the interrelationships between health data and purchase history data to understand the needs of health-conscious consumers. For example, the market research department can analyze the interrelationships between location data and social media activity to identify consumer interests in each region. By considering the interrelationships between data, the accuracy of the research can be improved. Some or all of the above processes in the market research department may be performed using AI, for example, or not using AI. For example, the market research department can input the interrelationships between data into a generating AI and have the generating AI perform the task of improving the accuracy of the research.

[0057] The market research department can conduct market research while considering the attribute information of data submitters. For example, the market research department can conduct market research on specific target groups based on attribute information such as age and gender. For example, the market research department can conduct market research on high-purchasing-power groups based on attribute information such as occupation and income. For example, the market research department can conduct regional market research based on attribute information such as place of residence and family structure. In this way, by considering the attribute information of data submitters, market research on specific target groups can be conducted efficiently. Some or all of the above processes in the market research department may be performed using AI, for example, or not using AI. For example, the market research department can input the attribute information of data submitters into a generating AI and have the generating AI execute the research.

[0058] The market research department can conduct market research while considering the geographical distribution of data. For example, the market research department can analyze consumer purchasing patterns in a specific region. For example, the market research department can understand the needs of health-conscious consumers based on regional health data. For example, the market research department can identify region-specific consumer behavior based on regional location data. This allows for efficient research into region-specific consumer behavior by considering the geographical distribution of data. Some or all of the above processes in the market research department may be performed using AI, for example, or without AI. For example, the market research department can input the geographical distribution of data into a generating AI and have the generating AI execute the research.

[0059] The market research department can improve the accuracy of its research by referring to relevant literature on the data during market research. For example, the market research department can refer to academic papers related to purchase history data to increase the reliability of research results. For example, the market research department can refer to medical literature related to health data to improve the accuracy of research results. For example, the market research department can refer to geographical studies related to location data to increase the accuracy of research results. In this way, the accuracy of research can be improved by referring to relevant literature on the data. Some or all of the above processing in the market research department may be performed using AI, for example, or not using AI. For example, the market research department can input relevant literature into a generating AI and have the generating AI perform the improvement of research accuracy.

[0060] The product development department can analyze users' past consumer behavior during product development to select the optimal development method. For example, the product development department can develop high-demand products based on users' past purchase history. For example, the product development department can develop region-specific products based on users' past location data. For example, the product development department can develop health-conscious products based on users' past health data. In this way, by analyzing users' past consumer behavior, the optimal development method can be selected and products can be developed efficiently. Some or all of the above processes in the product development department may be performed using AI, for example, or without AI. For example, the product development department can input users' past consumer behavior data into a generating AI and have the generating AI select the optimal development method.

[0061] The product development department can customize the development process based on the user's current lifestyle. For example, if the user is busy, the product development department can develop an easy-to-use product. If the user is health-conscious, the product development department can develop a health-conscious product. If the user is traveling, the product development department can develop a highly portable product. By customizing the development process based on the user's current lifestyle, the product development department can efficiently develop products that meet the user's needs. Some or all of the above processes in the product development department may be performed using AI, for example, or not. For example, the product development department can input data on the user's current lifestyle into a generating AI and have the generating AI perform the customization of the development process.

[0062] The product development department can select the optimal development method based on the user's geographical location information during product development. For example, the product development department can develop region-specific products based on the needs of consumers in a particular region. For example, the product development department can develop products with high demand based on purchase history data for each region. For example, the product development department can develop health-oriented products based on health data for each region. In this way, by selecting the optimal development method based on the user's geographical location information, region-specific products can be developed efficiently. Some or all of the above processes in the product development department may be performed using AI, for example, or without AI. For example, the product development department can input the user's geographical location information into a generating AI and have the generating AI select the optimal development method.

[0063] The product development department can analyze users' social media activity during product development and propose development methods. For example, the product development department can develop new products based on products mentioned by users on social media. For example, the product development department can improve products based on feedback shared by users on social media. For example, the product development department can develop related products based on campaigns that users have participated in on social media. In this way, by analyzing users' social media activity, related products can be developed efficiently. Some or all of the above processes in the product development department may be performed using AI, for example, or not using AI. For example, the product development department can input user social media activity data into a generating AI and have the generating AI propose development methods.

[0064] The reward provisioning unit can adjust the level of detail of rewards based on the frequency of data use when providing rewards. For example, the reward provisioning unit provides high rewards for frequently used data. For example, the reward provisioning unit provides standard rewards for commonly used data. For example, the reward provisioning unit provides low rewards for data used only occasionally. This allows the system to provide users with appropriate rewards by adjusting the level of detail of rewards based on the frequency of data use. Some or all of the above processing in the reward provisioning unit may be performed using AI, for example, or without AI. For example, the reward provisioning unit can input the frequency of data use into a generating AI and have the generating AI perform the adjustment of the level of detail of rewards.

[0065] The reward provision unit can apply different reward algorithms depending on the data category when providing rewards. For example, the reward provision unit can apply a medical reward algorithm to health data. For example, the reward provision unit can apply a commercial reward algorithm to purchase history data. For example, the reward provision unit can apply a location-specific reward algorithm to location data. By applying different reward algorithms depending on the data category, the system can provide users with appropriate rewards. Some or all of the above processing in the reward provision unit may be performed using AI, for example, or without AI. For example, the reward provision unit can input the data category into a generating AI and have the generating AI perform the application of the reward algorithm.

[0066] The reward provision unit can determine the priority of rewards based on when the data was used when providing rewards. For example, the reward provision unit may provide a higher reward for recently used data. For example, the reward provision unit may provide a standard reward for previously used data. For example, the reward provision unit may provide a reward appropriate to the period in which the data was used for data used. This allows the system to provide users with appropriate rewards by determining the priority of rewards based on when the data was used. Some or all of the above processing in the reward provision unit may be performed using AI, for example, or without AI. For example, the reward provision unit may input the data usage period into a generating AI and have the generating AI perform the determination of reward priorities.

[0067] The reward distribution unit can adjust the order of rewards based on the relevance of the data when providing rewards. For example, the reward distribution unit may provide high rewards for important data, standard rewards for general data, and low rewards for temporary data. By adjusting the order of rewards based on the relevance of the data, the reward distribution unit can provide users with appropriate rewards. Some or all of the above processing in the reward distribution unit may be performed using AI, for example, or without AI. For example, the reward distribution unit may input the relevance of the data into a generating AI and have the generating AI perform the adjustment of the order of rewards.

[0068] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0069] The data delivery system can analyze a user's past data delivery history and select the optimal data delivery method. For example, it can prioritize providing the types of data that the user has frequently provided in the past. It can also prioritize suggesting data delivery methods that the user has used in the past (manual, sensor, etc.). Furthermore, it can select the optimal data delivery method for a specific time period based on the user's past data delivery history. In this way, by analyzing the user's past data delivery history, the system can select the optimal data delivery method and deliver data efficiently.

[0070] The data delivery system can filter data based on the user's current activities and areas of interest when providing data. For example, if a user is exercising, health data can be prioritized. If a user is shopping, purchase history data can be prioritized. Furthermore, if a user is traveling, location data can be prioritized. By filtering data based on the user's current activities and areas of interest, the system can provide highly relevant data.

[0071] The data provision system can prioritize providing highly relevant data based on the user's geographical location. For example, if a user is in a specific region, data related to that region can be prioritized. Similarly, if a user is traveling, location data for their travel destination can be prioritized. Furthermore, if a user is participating in a specific event, data related to that event can be prioritized. This allows for the efficient provision of region-specific data by prioritizing highly relevant data based on the user's geographical location.

[0072] The data delivery system can adjust the level of detail provided based on the company's needs. For example, if a company requires detailed data, it can provide detailed data. If a company requires general data, it can provide standard data. Furthermore, if a company requires simplified data, it can provide simplified data. By adjusting the level of detail provided based on the company's needs, the system can deliver the most suitable data to each company.

[0073] The data provision system can apply different provision algorithms depending on the industry of the company when providing data. For example, a provision algorithm specifically for medical data can be applied to the medical industry. Similarly, a provision algorithm specifically for purchase history data can be applied to the retail industry. Furthermore, a provision algorithm specifically for location information data can be applied to the travel industry. This allows for industry-specific data provision by applying different provision algorithms according to the company's industry.

[0074] The data provision system can prioritize providing highly relevant data based on a company's geographical location. For example, if a company needs data related to a specific region, it can prioritize providing data for that region. Similarly, if a company needs global data, it can provide data covering a wide range of areas. Furthermore, if a company needs data related to a specific city, it can prioritize providing data for that city. This allows for the efficient provision of region-specific data by prioritizing highly relevant data based on a company's geographical location.

[0075] The following briefly describes the processing flow for example form 1.

[0076] Step 1: The data collection unit collects data from the user, such as purchase history, location information, and health data. For example, purchase history includes the type of product purchased, the date and time of purchase, and the location of purchase; location information includes GPS data, Wi-Fi location information, and beacon data; and health data includes heart rate, steps taken, and sleep data. Step 2: The data protection unit securely manages the data collected by the collection unit. For example, it encrypts the collected data for secure management, implements access control, and performs regular data backups. Step 3: The data provision department provides the data managed by the data protection department to the company. For example, it provides data to the company in real time, provides data in a specified format, and sets the frequency of data provision. Step 4: The market research department uses the data provided by the data provision department to conduct market research and product development. For example, they analyze consumer behavior patterns based on the provided data, understand consumer needs, and identify consumer problems. Step 5: The Product Development Department improves product development and marketing strategies based on the results obtained by the Market Research Department. For example, they develop products, improve marketing strategies, and develop new products based on the analysis results. Step 6: The rewards department provides rewards to users based on the data provided by the data department. For example, it may provide points, coupons, or cash to users each time the data is used.

[0077] (Example of form 2) The data provision system according to an embodiment of the present invention is a system in which individuals can directly provide their first-party data to companies and receive appropriate compensation for the use of that data. The data provision system allows users to securely share purchase history, location information, health data, etc., through an application, and accumulate points each time the data is used. Companies can utilize this real-time data for market research, product development, and personalized marketing. Furthermore, companies can have individuals answer detailed questionnaires about their lifestyle and consumption behavior, allowing them to understand consumer needs and problems and use this information to improve product development and marketing strategies. For example, the data provision system allows users to provide their data through an application, such as purchase history, location information, and health data. This data is securely shared, and users earn points each time the data is used. These points are received by users as compensation and can be used, for example, as points in an electronic payment system. Next, the data provision system allows companies to utilize the data provided by users in real time. For example, it can be used for market research, product development, and personalized marketing. Based on users' purchase history and location information, companies can analyze consumer behavior patterns and formulate optimal marketing strategies. Furthermore, companies conduct detailed surveys of users regarding their lifestyles and consumption behavior to understand consumer needs and problems. They also use the data obtained from users to improve product development and marketing strategies. For example, they analyze users' purchase history to identify products and services that consumers desire. They also utilize users' location information to understand regional consumption behavior and develop region-specific marketing strategies. This allows data provision systems to enable users to control their own data while receiving appropriate compensation. Companies can also efficiently acquire high-quality first-party data and use it to improve the quality of their products and services. This results in a system that benefits both individuals and businesses.This allows the data provision system to securely manage user data and provide it to companies, which can then use it for market research and product development, while users can earn rewards.

[0078] The data provision system according to this embodiment comprises a collection unit, a data protection unit, a data provision unit, a market research unit, a product development unit, and a reward provision unit. The collection unit collects data from users, such as purchase history, location information, and health data. For example, the collection unit collects the user's purchase history. Purchase history includes the type of product purchased, the date and time of purchase, and the location of purchase. For example, the collection unit collects the user's location information. Location information includes GPS data, Wi-Fi location information, beacon data, etc. The collection unit collects the user's health data. Health data includes heart rate, steps taken, sleep data, etc. The data protection unit securely manages the data collected by the collection unit. For example, the data protection unit encrypts the collected data for secure management. Algorithms such as AES, RSA, and SHA are used for encryption. For example, the data protection unit performs access control to the collected data. Access control includes user authentication and authorization management. For example, the data protection unit backs up the collected data. Backups include regular data copying and recovery processes. The Data Provision Department provides data managed by the Data Protection Department to companies. The Data Provision Department provides data to companies in real time, for example. Real time includes data delivery delay time and update frequency. The Data Provision Department provides data to companies in specified formats, for example. Data formats include CSV, JSON, XML, etc. The Data Provision Department sets the data delivery frequency for companies, for example. Delivery frequency includes real time, periodic, on-demand, etc. The Market Research Department uses the data provided by the Data Provision Department to conduct market research and product development. The Market Research Department analyzes consumer behavior patterns based on the provided data, for example. Analysis of behavior patterns includes clustering, time series analysis, and behavioral forecasting. The Market Research Department understands consumer needs based on the provided data, for example. Understanding consumer needs includes surveys, data analysis, and consumer interviews. The Market Research Department identifies consumer problems based on the provided data, for example.Identifying consumer issues includes data mining, text mining, and feedback analysis. The product development department improves product development and marketing strategies based on the results obtained by the market research department. For example, the product development department develops products based on the analysis results. Product development includes prototyping, user testing, and feedback collection. The product development department improves marketing strategies based on the analysis results. Improving marketing strategies includes A / B testing, user feedback, and performance evaluation. The product development department develops new products based on the analysis results. New product development includes concept design, prototyping, and mass production. The rewards department provides rewards to users based on the data provided by the data provision department. For example, the rewards department provides points to users each time data is used. Points include criteria for awarding points, usage methods, and exchangeable items. The rewards department provides coupons to users. Coupons include discount coupons, free coupons, and special offer coupons. The rewards department provides cash to users. Cash includes bank transfers, electronic money, and gift cards. As a result, the data provision system according to this embodiment securely manages user data and provides it to companies, allowing companies to utilize it for market research and product development, while users can earn rewards.

[0079] The data collection unit collects data from users, including purchase history, location information, and health data. Specifically, when collecting a user's purchase history, detailed information such as the type of product purchased, the date and time of purchase, and the location of purchase is included. This allows for an understanding of the user's purchasing trends and preferences. Location information collection includes GPS data, Wi-Fi location information, and beacon data, allowing for the identification of the user's movement patterns and visited locations. For example, if a user frequently visits a particular store, it becomes possible to provide promotional information for that store. Health data collection includes heart rate, step count, and sleep data, allowing for an understanding of the user's health status and lifestyle habits. This enables the suggestion of health-related products and services. The data collection unit centrally manages this data and updates it in real time, ensuring that users always have access to the latest information. Furthermore, the data collection unit can flexibly adjust the data collection method and frequency, efficiently collecting necessary data while respecting user privacy. For example, if a user refuses to provide certain data, the system can be configured not to collect that data. This allows the data collection unit to effectively collect necessary data while gaining the user's trust.

[0080] The Data Protection Department securely manages the data collected by the Collection Department. Specifically, it encrypts the collected data for secure management. Advanced encryption algorithms such as AES, RSA, and SHA are used for encryption to prevent unauthorized access and leakage of data. The Data Protection Department implements access control to the collected data, strictly restricting access to the data through user authentication and permission management. For example, it can be configured so that only users with specific permissions can access specific data. Furthermore, the Data Protection Department regularly backs up the collected data to prepare for data loss or corruption. Backups involve storing copies of the data in multiple locations and establishing a recovery process to ensure a quick response in the event of an emergency. The Data Protection Department stays informed about the latest technologies and regulations regarding data protection and continuously improves the security of the system. For example, introducing new encryption technologies and security protocols can further enhance data security. This allows the Data Protection Department to securely manage user data and realize a reliable data delivery system.

[0081] The Data Provision Department provides companies with data managed by the Data Protection Department. Specifically, it provides companies with real-time data. Real-time data provision minimizes data delivery delays and increases update frequency, enabling companies to quickly utilize the latest data. The Data Provision Department provides companies with data in specified formats. These formats include CSV, JSON, XML, etc., allowing data to be provided in a format suitable for the company's systems and applications. Furthermore, the Data Provision Department sets the data provision frequency for companies. This frequency includes real-time, periodic, and on-demand options, enabling flexible data provision tailored to the company's needs. For example, data can be provided in real-time during specific campaign periods and updated regularly during normal times. The Data Provision Department strengthens collaboration with companies and builds a feedback loop to maintain data quality and accuracy. This allows companies to conduct market research and product development based on the provided data, and the Data Provision Department to improve data provision based on feedback from companies. In this way, the Data Provision Department can provide companies with high-quality data and support their business growth.

[0082] The Market Research Department conducts market research and product development using data provided by the Data Provision Department. Specifically, it analyzes consumer behavior patterns based on the provided data. Advanced data analysis techniques such as clustering, time series analysis, and behavioral prediction are used to analyze behavioral patterns, allowing for a detailed understanding of consumer purchasing trends and preferences. The Market Research Department also understands consumer needs based on the provided data. This includes surveys, data analysis, and consumer interviews, enabling the identification of products and services that consumers desire. Furthermore, the Market Research Department identifies consumer problems based on the provided data. Data mining, text mining, and feedback analysis are used to identify consumer problems, revealing the challenges and frustrations that consumers face. Based on these analysis results, the Market Research Department provides companies with concrete improvement suggestions and new product development ideas. In this way, the Market Research Department contributes to improving companies' product development and marketing strategies, and enhances consumer satisfaction.

[0083] The Product Development Department improves product development and marketing strategies based on the results obtained by the Market Research Department. Specifically, it develops products based on the analysis results. Product development includes prototyping, user testing, and feedback collection, enabling the rapid development of products that meet consumer needs. The Product Development Department also improves marketing strategies based on the analysis results. This includes A / B testing, user feedback, and performance evaluation, enabling the deployment of effective promotions and advertising campaigns. Furthermore, the Product Development Department develops new products based on the analysis results. New product development includes concept design, prototyping, and mass production, enabling the introduction of innovative products that meet consumer expectations. By strengthening collaboration with the Market Research Department and continuously collecting consumer feedback, the Product Development Department can improve the quality of products and services. This allows the Product Development Department to enhance the company's competitiveness and achieve sustainable growth.

[0084] The rewards department provides rewards to users based on the data provided by the data department. Specifically, users receive points each time their data is used. Points include criteria for awarding points, usage methods, and exchangeable items, allowing users to accumulate points and exchange them for various benefits. The rewards department also provides coupons to users. These coupons include discount coupons, free coupons, and special offer coupons, allowing users to use products and services at a discount. Furthermore, the rewards department provides cash to users. This cash can be received via bank transfer, electronic money, gift cards, etc., allowing users to receive rewards according to their preferences. The rewards department can continuously review and improve the types and methods of rewards to enhance user satisfaction. For example, introducing new reward programs or reviewing reward awarding criteria can increase user participation. This allows the rewards department to strengthen user incentives for data provision and improve the overall data collection efficiency of the system.

[0085] The data collection unit can collect user purchase history, location information, health data, etc. For example, the data collection unit can collect user purchase history. Purchase history includes the type of product purchased, the date and time of purchase, and the location of purchase. For example, the data collection unit can collect user location information. Location information includes GPS data, Wi-Fi location information, beacon data, etc. For example, the data collection unit can collect user health data. Health data includes heart rate, steps taken, sleep data, etc. By collecting user purchase history, location information, health data, etc., companies can obtain detailed data. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input user purchase history data into a generating AI and have the generating AI perform analysis of the purchase history.

[0086] The data protection unit can encrypt and securely manage the collected data. For example, the data protection unit encrypts and securely manages the collected data. Algorithms such as AES, RSA, and SHA are used for encryption. For example, the data protection unit performs access control to the collected data. Access control includes user authentication and permission management. For example, the data protection unit backs up the collected data. Backups include periodic data copying and recovery processes. By encrypting and securely managing the collected data, data leakage can be prevented. Some or all of the above processes in the data protection unit may be performed using AI, for example, or without AI. For example, the data protection unit can input collected data into a generating AI and have the generating AI perform data encryption.

[0087] The data provision department can provide data to companies in real time. For example, the data provision department can provide data to companies in real time. Real time includes data provision delay time, update frequency, etc. The data provision department can provide data to companies in a specified format. Data formats include CSV, JSON, XML, etc. The data provision department can set the frequency of data provision to companies. Provision frequency includes real time, periodic, on-demand, etc. By providing data to companies in real time, companies can utilize the latest data. Some or all of the above processing in the data provision department may be performed using AI, for example, or not using AI. For example, the data provision department can input the data to be provided to companies into a generating AI and have the generating AI perform the data provision.

[0088] The market research department can analyze consumer behavior patterns based on the provided data. For example, the market research department can analyze consumer behavior patterns based on the provided data. Analysis of behavior patterns includes clustering, time series analysis, and behavioral forecasting. For example, the market research department can understand consumer needs based on the provided data. Understanding consumer needs includes surveys, data analysis, and consumer interviews. For example, the market research department can identify consumer problems based on the provided data. Identifying consumer problems includes data mining, text mining, and feedback analysis. In this way, by analyzing consumer behavior patterns based on the provided data, companies can understand consumer needs. Some or all of the above processes in the market research department may be performed using AI, for example, or not using AI. For example, the market research department can input the provided data into a generating AI and have the generating AI perform an analysis of consumer behavior patterns.

[0089] The product development department can improve product development and marketing strategies based on the analysis results. For example, the product development department can develop products based on the analysis results. Product development includes prototyping, user testing, and feedback collection. For example, the product development department can improve marketing strategies based on the analysis results. Improvement of marketing strategies includes A / B testing, user feedback, and performance evaluation. For example, the product development department can develop new products based on the analysis results. New product development includes concept design, prototyping, and mass production. By improving product development and marketing strategies based on analysis results, companies can formulate more effective products and strategies. Some or all of the above processes in the product development department may be performed using AI, for example, or not. For example, the product development department can input analysis results into a generating AI and have the generating AI perform improvements to product development and marketing strategies.

[0090] The rewards department can provide users with points each time their data is used. For example, the rewards department can provide users with points each time their data is used. Points include criteria for awarding points, how to use them, and items they can be exchanged for. The rewards department can provide users with coupons, for example. Coupons include discount coupons, free coupons, and special offer coupons. The rewards department can provide users with cash, for example. Cash includes bank transfers, electronic money, and gift cards. This way, by providing users with points each time their data is used, users can have an incentive to provide data. Some or all of the above processing in the rewards department may be performed using AI, for example, or not using AI. For example, the rewards department can input data usage information into a generating AI and have the generating AI award points.

[0091] The market research department can conduct surveys with users regarding their lifestyles and consumer behavior. For example, the market research department might conduct surveys with users regarding their lifestyles and consumer behavior. These surveys might include questions about daily activities, consumer behavior, hobbies, and preferences. Based on the survey results, the market research department might understand consumer needs. This understanding could include statistical analysis, text mining, and cross-tabulation. Based on the survey results, the market research department might identify consumer problems. This identification could include data mining, text mining, and feedback analysis. This allows companies to understand the detailed needs of consumers by conducting surveys with users regarding their lifestyles and consumer behavior. Some or all of the above processes in the market research department may be performed using AI, or not. For example, the market research department could input survey results into a generating AI and have the generating AI perform the task of understanding consumer needs.

[0092] The product development department can improve product development and marketing strategies based on survey results. For example, the product development department can develop products based on survey results. Product development includes prototyping, user testing, and feedback collection. The product development department can improve marketing strategies based on survey results. Improvement of marketing strategies includes A / B testing, user feedback, and performance evaluation. The product development department can develop new products based on survey results. New product development includes concept design, prototyping, and mass production. By improving product development and marketing strategies based on survey results, companies can formulate products and strategies that meet consumer needs. Some or all of the above processes in the product development department may be performed using AI, for example, or not. For example, the product development department can input survey results into a generating AI and have the generating AI perform improvements to product development and marketing strategies.

[0093] The data collection unit can estimate the user's emotions and adjust the timing of data collection based on the estimated emotions. For example, if the user is stressed, the data collection unit can temporarily stop data collection and resume it when the user is relaxed. For example, if the user is relaxed, the data collection unit can actively collect detailed data. For example, if the user is in a hurry, the data collection unit can minimize data collection and collect detailed data later. This reduces the burden on the user and allows for the collection of more accurate data by adjusting the timing of data collection based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input user emotion data into a generative AI and have the generative AI adjust the timing of data collection.

[0094] The data collection unit can analyze the user's past data provision history and select the optimal data collection method. For example, the data collection unit may prioritize collecting the types of data that the user has frequently provided in the past. For example, the data collection unit may prioritize suggesting data collection methods that the user has used in the past (manual, sensor, etc.). For example, the data collection unit may select the optimal data collection method for a specific time period based on the user's past data provision history. This allows for efficient data collection by analyzing the user's past data provision history and selecting the optimal data collection method. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's past data provision history into a generating AI and have the generating AI select the optimal data collection method.

[0095] The data collection unit can filter data based on the user's current activities and areas of interest during data collection. For example, if the user is exercising, the data collection unit will prioritize collecting health data. For example, if the user is shopping, the data collection unit will prioritize collecting purchase history data. For example, if the user is traveling, the data collection unit will prioritize collecting location information data. By filtering the data based on the user's current activities and areas of interest, it is possible to collect highly relevant data. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's current activities and areas of interest into a generating AI and have the generating AI perform data filtering.

[0096] The data collection unit can estimate the user's emotions and determine the priority of data to collect based on the estimated user emotions. For example, if the user is stressed, the data collection unit will prioritize collecting stress-related health data. For example, if the user is relaxed, the data collection unit will prioritize collecting data related to daily life. For example, if the user is excited, the data collection unit will prioritize collecting data related to the cause of the excitement. In this way, by determining the priority of data to collect based on the user's emotions, data that meets the user's needs can be collected. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI, for example, or not using AI. For example, the data collection unit can input user emotion data into a generative AI and have the generative AI perform the determination of data priority.

[0097] The data collection unit can prioritize the collection of highly relevant data based on the user's geographical location information during data collection. For example, if the user is in a specific region, the data collection unit will prioritize the collection of purchase history data related to that region. For example, if the user is traveling, the data collection unit will prioritize the collection of location information data related to the travel destination. For example, if the user is participating in a specific event, the data collection unit will prioritize the collection of data related to that event. This allows for the efficient collection of region-specific data by prioritizing the collection of highly relevant data based on the user's geographical location information. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's geographical location information into a generating AI and have the generating AI perform the collection of highly relevant data.

[0098] The data collection unit can analyze a user's social media activity and collect relevant data during data collection. For example, the data collection unit can collect relevant purchase history data based on information shared by the user on social media. For example, the data collection unit can collect location data of places checked in by the user on social media. For example, the data collection unit can collect relevant health data based on health information mentioned by the user on social media. This allows for the efficient collection of relevant data by analyzing the user's social media activity. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's social media activity data into a generating AI and have the generating AI perform the collection of relevant data.

[0099] The data protection unit can estimate the user's emotions and adjust the level of data protection based on the estimated emotions. For example, if the user is feeling anxious, the data protection unit can increase the level of data protection. For example, if the user is relaxed, the data protection unit can maintain the normal level of data protection. For example, if the user is in a hurry, the data protection unit can temporarily lower the level of data protection. This allows for an increase in the user's sense of security by adjusting the level of data protection based on their emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data protection unit may be performed using AI or not using AI. For example, the data protection unit can input user emotion data into a generative AI and have the generative AI adjust the level of data protection.

[0100] The data protection unit can adjust the encryption strength based on the importance of the collected data during data protection. For example, the data protection unit applies strong encryption to important data such as health data. For example, it applies standard encryption to common data such as purchase history data. For example, it applies light encryption to temporary data such as location data. This optimizes the level of data protection by adjusting the encryption strength based on the importance of the collected data. Some or all of the above processing in the data protection unit may be performed using AI, for example, or without AI. For example, the data protection unit can input the importance of the collected data into a generating AI and have the generating AI adjust the encryption strength.

[0101] The data protection unit can apply different protection algorithms depending on the data category during data protection. For example, the data protection unit can apply an advanced medical protection algorithm to health data. For example, it can apply a standard commercial protection algorithm to purchase history data. For example, it can apply a location-specific protection algorithm to location data. By applying different protection algorithms depending on the data category, the accuracy of data protection can be improved. Some or all of the above processing in the data protection unit may be performed using AI, for example, or without AI. For example, the data protection unit can input the data category into a generating AI and have the generating AI execute the application of the protection algorithm.

[0102] The data protection unit can estimate the user's emotions and determine data protection priorities based on those emotions. For example, if the user is feeling anxious, the data protection unit will prioritize the protection of important data. If the user is relaxed, the data protection unit will perform normal data protection. If the user is in a hurry, the data protection unit will temporarily lower the priority of data protection. This increases the user's sense of security by determining data protection priorities based on their emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data protection unit may be performed using AI or not. For example, the data protection unit can input user emotion data into a generative AI and have the generative AI determine the data protection priorities.

[0103] The data protection unit can determine the priority of protection based on when the data was submitted. For example, the data protection unit may prioritize the protection of recently collected data. For example, it may apply normal protection to data collected in the past. For example, it may apply protection appropriate to data collected during a specific period. This allows for the priority of protecting the most recent data by determining the priority of protection based on when the data was submitted. Some or all of the above processing in the data protection unit may be performed using AI, for example, or without AI. For example, the data protection unit may input the data submission date into a generating AI and have the generating AI perform the determination of the protection priority.

[0104] The data protection unit can adjust the order of protection based on the relevance of the data during data protection. For example, the data protection unit may prioritize the protection of important data. For example, it may perform normal protection on general data. For example, it may perform light protection on temporary data. This allows for priority protection of important data by adjusting the order of protection based on the relevance of the data. Some or all of the above processing in the data protection unit may be performed using AI, for example, or without AI. For example, the data protection unit may input the relevance of the data into a generating AI and have the generating AI perform the adjustment of the order of protection.

[0105] The data provision unit can estimate the user's emotions and adjust the timing of data provision based on the estimated emotions. For example, if the user is relaxed, the data provision unit will actively provide data. For example, if the user is stressed, the data provision unit will temporarily stop providing data. For example, if the user is in a hurry, the data provision unit will minimize data provision. By adjusting the timing of data provision based on the user's emotions, the burden on the user is reduced and data can be provided efficiently. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data provision unit may be performed using AI, for example, or not using AI. For example, the data provision unit can input user emotion data into a generative AI and have the generative AI adjust the timing of data provision.

[0106] The data provision unit can adjust the level of detail of the data provided based on the company's needs. For example, if a company requires detailed data, the data provision unit will provide detailed data. For example, if a company requires general data, the data provision unit will provide standard data. For example, if a company requires simplified data, the data provision unit will provide simplified data. In this way, by adjusting the level of detail of the data provided based on the company's needs, the data provision unit can provide the most suitable data for the company. Some or all of the above processing in the data provision unit may be performed using AI, for example, or without AI. For example, the data provision unit can input the company's needs into a generating AI and have the generating AI perform the adjustment of the level of detail of the data.

[0107] The data provision unit can apply different provision algorithms depending on the industry of the company when providing data. For example, the data provision unit can apply a provision algorithm specifically for medical data to the medical industry. For example, the data provision unit can apply a provision algorithm specifically for purchase history data to the retail industry. For example, the data provision unit can apply a provision algorithm specifically for location information data to the travel industry. By applying different provision algorithms depending on the industry of the company, it becomes possible to provide industry-specific data. Some or all of the above processing in the data provision unit may be performed using AI, for example, or without using AI. For example, the data provision unit can input the industry of the company into a generating AI and have the generating AI execute the application of the provision algorithm.

[0108] The data provision unit can estimate the user's emotions and determine the priority of data provision based on the estimated emotions. For example, the data provision unit will prioritize data provision if the user is relaxed. For example, the data provision unit will temporarily suspend data provision if the user is stressed. For example, the data provision unit will temporarily lower the priority of data provision if the user is in a hurry. By determining the priority of data provision based on the user's emotions, the burden on the user can be reduced and data can be provided efficiently. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data provision unit may be performed using AI or not using AI. For example, the data provision unit can input user emotion data into a generative AI and have the generative AI perform the determination of data provision priorities.

[0109] The data provision unit can prioritize providing highly relevant data based on a company's geographical location information when providing data. For example, if a company needs data related to a specific region, the data provision unit will prioritize providing data for that region. For example, if a company needs global data, the data provision unit will provide broad-ranging data. For example, if a company needs data related to a specific city, the data provision unit will prioritize providing data for that city. This allows for the efficient provision of region-specific data by prioritizing the provision of highly relevant data based on a company's geographical location information. Some or all of the above processing in the data provision unit may be performed using AI, for example, or without AI. For example, the data provision unit can input a company's geographical location information into a generating AI and have the generating AI perform the task of providing highly relevant data.

[0110] The data provision unit can analyze a company's social media activities and provide relevant data when providing data. For example, the data provision unit can provide data related to topics mentioned by the company on social media. For example, the data provision unit can provide data related to campaigns conducted by the company on social media. For example, the data provision unit can provide relevant data based on information shared by the company on social media. This allows for the efficient provision of relevant data by analyzing a company's social media activities. Some or all of the above processing in the data provision unit may be performed using AI, for example, or without AI. For example, the data provision unit can input a company's social media activity data into a generating AI and have the generating AI perform the provision of relevant data.

[0111] The market research department can estimate user emotions and adjust market research criteria based on those estimated emotions. For example, if a user is relaxed, the market research department can conduct a detailed market research. For example, if a user is stressed, the market research department can conduct a simple market research. For example, if a user is in a hurry, the market research department can conduct a market research that can be completed in a short time. By adjusting market research criteria based on user emotions, the burden on users can be reduced and market research can be conducted efficiently. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the market research department may be performed using AI or not. For example, the market research department can input user emotion data into a generative AI and have the generative AI adjust the market research criteria.

[0112] The market research department can improve the accuracy of its research by considering the interrelationships between data. For example, the market research department can analyze the interrelationships between purchase history data and location data to clarify consumer behavior patterns. For example, the market research department can analyze the interrelationships between health data and purchase history data to understand the needs of health-conscious consumers. For example, the market research department can analyze the interrelationships between location data and social media activity to identify consumer interests in each region. By considering the interrelationships between data, the accuracy of the research can be improved. Some or all of the above processes in the market research department may be performed using AI, for example, or not using AI. For example, the market research department can input the interrelationships between data into a generating AI and have the generating AI perform the task of improving the accuracy of the research.

[0113] The market research department can conduct market research while considering the attribute information of data submitters. For example, the market research department can conduct market research on specific target groups based on attribute information such as age and gender. For example, the market research department can conduct market research on high-purchasing-power groups based on attribute information such as occupation and income. For example, the market research department can conduct regional market research based on attribute information such as place of residence and family structure. In this way, by considering the attribute information of data submitters, market research on specific target groups can be conducted efficiently. Some or all of the above processes in the market research department may be performed using AI, for example, or not using AI. For example, the market research department can input the attribute information of data submitters into a generating AI and have the generating AI execute the research.

[0114] The market research department can estimate the user's emotions and adjust the order in which market research results are displayed based on the estimated user emotions. For example, if the user is relaxed, the market research department can prioritize displaying detailed results. For example, if the user is stressed, the market research department can prioritize displaying concise results. For example, if the user is in a hurry, the market research department can prioritize displaying brief results. By adjusting the order in which market research results are displayed based on the user's emotions, the burden on the user is reduced, and results can be reviewed efficiently. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the market research department may be performed using AI or not using AI. For example, the market research department can input user emotion data into a generative AI and have the generative AI adjust the order in which results are displayed.

[0115] The market research department can conduct market research while considering the geographical distribution of data. For example, the market research department can analyze consumer purchasing patterns in a specific region. For example, the market research department can understand the needs of health-conscious consumers based on regional health data. For example, the market research department can identify region-specific consumer behavior based on regional location data. This allows for efficient research into region-specific consumer behavior by considering the geographical distribution of data. Some or all of the above processes in the market research department may be performed using AI, for example, or without AI. For example, the market research department can input the geographical distribution of data into a generating AI and have the generating AI execute the research.

[0116] The market research department can improve the accuracy of its research by referring to relevant literature on the data during market research. For example, the market research department can refer to academic papers related to purchase history data to increase the reliability of research results. For example, the market research department can refer to medical literature related to health data to improve the accuracy of research results. For example, the market research department can refer to geographical studies related to location data to increase the accuracy of research results. In this way, the accuracy of research can be improved by referring to relevant literature on the data. Some or all of the above processing in the market research department may be performed using AI, for example, or not using AI. For example, the market research department can input relevant literature into a generating AI and have the generating AI perform the improvement of research accuracy.

[0117] The product development department can estimate the user's emotions and adjust the product development process based on those emotions. For example, if the user is relaxed, the product development department can develop the product based on detailed feedback. If the user is stressed, the product development department can develop the product based on simple feedback. If the user is in a hurry, the product development department can develop the product based on quick feedback. By adjusting the product development process based on the user's emotions, the product development department can efficiently develop products that meet the user's needs. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processes in the product development department may be performed using AI or not. For example, the product development department can input user emotion data into a generative AI and have the generative AI adjust the product development process.

[0118] The product development department can analyze users' past consumer behavior during product development to select the optimal development method. For example, the product development department can develop high-demand products based on users' past purchase history. For example, the product development department can develop region-specific products based on users' past location data. For example, the product development department can develop health-conscious products based on users' past health data. In this way, by analyzing users' past consumer behavior, the optimal development method can be selected and products can be developed efficiently. Some or all of the above processes in the product development department may be performed using AI, for example, or without AI. For example, the product development department can input users' past consumer behavior data into a generating AI and have the generating AI select the optimal development method.

[0119] The product development department can customize the development process based on the user's current lifestyle. For example, if the user is busy, the product development department can develop an easy-to-use product. If the user is health-conscious, the product development department can develop a health-conscious product. If the user is traveling, the product development department can develop a highly portable product. By customizing the development process based on the user's current lifestyle, the product development department can efficiently develop products that meet the user's needs. Some or all of the above processes in the product development department may be performed using AI, for example, or not. For example, the product development department can input data on the user's current lifestyle into a generating AI and have the generating AI perform the customization of the development process.

[0120] The product development department can estimate user emotions and determine product development priorities based on those estimated emotions. For example, if the user is relaxed, the product development department will prioritize product development based on detailed feedback. If the user is stressed, the product development department will prioritize product development based on simple feedback. If the user is in a hurry, the product development department will prioritize product development based on quick feedback. This allows for the efficient development of products that meet user needs by determining product development priorities based on user emotions. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the product development department may be performed using AI or not. For example, the product development department can input user emotion data into a generative AI and have the generative AI determine product development priorities.

[0121] The product development department can select the optimal development method based on the user's geographical location information during product development. For example, the product development department can develop region-specific products based on the needs of consumers in a particular region. For example, the product development department can develop products with high demand based on purchase history data for each region. For example, the product development department can develop health-oriented products based on health data for each region. In this way, by selecting the optimal development method based on the user's geographical location information, region-specific products can be developed efficiently. Some or all of the above processes in the product development department may be performed using AI, for example, or without AI. For example, the product development department can input the user's geographical location information into a generating AI and have the generating AI select the optimal development method.

[0122] The product development department can analyze users' social media activity during product development and propose development methods. For example, the product development department can develop new products based on products mentioned by users on social media. For example, the product development department can improve products based on feedback shared by users on social media. For example, the product development department can develop related products based on campaigns that users have participated in on social media. In this way, by analyzing users' social media activity, related products can be developed efficiently. Some or all of the above processes in the product development department may be performed using AI, for example, or not using AI. For example, the product development department can input user social media activity data into a generating AI and have the generating AI propose development methods.

[0123] The reward system can estimate the user's emotions and adjust the reward method based on the estimated emotions. For example, if the user is relaxed, the reward system may suggest a detailed reward method. If the user is stressed, the reward system may suggest a simple reward method. If the user is in a hurry, the reward system may suggest a quick reward method. By adjusting the reward method based on the user's emotions, user satisfaction can be increased. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the reward system may be performed using AI or not. For example, the reward system can input user emotion data into a generative AI and have the generative AI adjust the reward method.

[0124] The reward provisioning unit can adjust the level of detail of rewards based on the frequency of data use when providing rewards. For example, the reward provisioning unit provides high rewards for frequently used data. For example, the reward provisioning unit provides standard rewards for commonly used data. For example, the reward provisioning unit provides low rewards for data used only occasionally. This allows the system to provide users with appropriate rewards by adjusting the level of detail of rewards based on the frequency of data use. Some or all of the above processing in the reward provisioning unit may be performed using AI, for example, or without AI. For example, the reward provisioning unit can input the frequency of data use into a generating AI and have the generating AI perform the adjustment of the level of detail of rewards.

[0125] The reward provision unit can apply different reward algorithms depending on the data category when providing rewards. For example, the reward provision unit can apply a medical reward algorithm to health data. For example, the reward provision unit can apply a commercial reward algorithm to purchase history data. For example, the reward provision unit can apply a location-specific reward algorithm to location data. By applying different reward algorithms depending on the data category, the system can provide users with appropriate rewards. Some or all of the above processing in the reward provision unit may be performed using AI, for example, or without AI. For example, the reward provision unit can input the data category into a generating AI and have the generating AI perform the application of the reward algorithm.

[0126] The reward provision unit can estimate the user's emotions and determine the priority of reward provision based on the estimated user emotions. For example, if the user is relaxed, the reward provision unit will prioritize detailed rewards. For example, if the user is stressed, the reward provision unit will prioritize simple rewards. For example, if the user is in a hurry, the reward provision unit will prioritize quick rewards. By determining the priority of reward provision based on the user's emotions, user satisfaction can be increased. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reward provision unit may be performed using AI or not using AI. For example, the reward provision unit can input user emotion data into a generative AI and have the generative AI determine the priority of reward provision.

[0127] The reward provision unit can determine the priority of rewards based on when the data was used when providing rewards. For example, the reward provision unit may provide a higher reward for recently used data. For example, the reward provision unit may provide a standard reward for previously used data. For example, the reward provision unit may provide a reward appropriate to the period in which the data was used for data used. This allows the system to provide users with appropriate rewards by determining the priority of rewards based on when the data was used. Some or all of the above processing in the reward provision unit may be performed using AI, for example, or without AI. For example, the reward provision unit may input the data usage period into a generating AI and have the generating AI perform the determination of reward priorities.

[0128] The reward distribution unit can adjust the order of rewards based on the relevance of the data when providing rewards. For example, the reward distribution unit may provide high rewards for important data, standard rewards for general data, and low rewards for temporary data. By adjusting the order of rewards based on the relevance of the data, the reward distribution unit can provide users with appropriate rewards. Some or all of the above processing in the reward distribution unit may be performed using AI, for example, or without AI. For example, the reward distribution unit may input the relevance of the data into a generating AI and have the generating AI perform the adjustment of the order of rewards.

[0129] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0130] The data delivery system can estimate the user's emotions and adjust the timing of data delivery based on those emotions. For example, if the user is stressed, data delivery can be temporarily suspended and resumed when the user is relaxed. Conversely, if the user is relaxed, data delivery can be actively provided, offering detailed information. Furthermore, if the user is in a hurry, data delivery can be minimized, with more detailed information provided later. By adjusting the timing of data delivery based on the user's emotions, the system can reduce the user's burden and deliver data efficiently.

[0131] The data delivery system can analyze a user's past data delivery history and select the optimal data delivery method. For example, it can prioritize providing the types of data that the user has frequently provided in the past. It can also prioritize suggesting data delivery methods that the user has used in the past (manual, sensor, etc.). Furthermore, it can select the optimal data delivery method for a specific time period based on the user's past data delivery history. In this way, by analyzing the user's past data delivery history, the system can select the optimal data delivery method and deliver data efficiently.

[0132] The data delivery system can filter data based on the user's current activities and areas of interest when providing data. For example, if a user is exercising, health data can be prioritized. If a user is shopping, purchase history data can be prioritized. Furthermore, if a user is traveling, location data can be prioritized. By filtering data based on the user's current activities and areas of interest, the system can provide highly relevant data.

[0133] The data provision system can estimate the user's emotions and prioritize data provision based on those emotions. For example, if a user is stressed, stress-related data can be prioritized. If the user is relaxed, data related to daily life can be prioritized. Furthermore, if the user is agitated, data related to the cause of the agitation can be prioritized. In this way, by prioritizing data provision based on the user's emotions, data tailored to the user's needs can be provided.

[0134] The data provision system can prioritize providing highly relevant data based on the user's geographical location. For example, if a user is in a specific region, data related to that region can be prioritized. Similarly, if a user is traveling, location data for their travel destination can be prioritized. Furthermore, if a user is participating in a specific event, data related to that event can be prioritized. This allows for the efficient provision of region-specific data by prioritizing highly relevant data based on the user's geographical location.

[0135] The data delivery system can estimate the user's emotions and adjust the data delivery method based on those emotions. For example, if the user is relaxed, it can suggest a detailed data delivery method. If the user is stressed, it can suggest a simplified data delivery method. Furthermore, if the user is in a hurry, it can suggest a rapid data delivery method. By adjusting the data delivery method based on the user's emotions, this system can increase user satisfaction.

[0136] The data delivery system can adjust the level of detail provided based on the company's needs. For example, if a company requires detailed data, it can provide detailed data. If a company requires general data, it can provide standard data. Furthermore, if a company requires simplified data, it can provide simplified data. By adjusting the level of detail provided based on the company's needs, the system can deliver the most suitable data to each company.

[0137] The data provision system can apply different provision algorithms depending on the industry of the company when providing data. For example, a provision algorithm specifically for medical data can be applied to the medical industry. Similarly, a provision algorithm specifically for purchase history data can be applied to the retail industry. Furthermore, a provision algorithm specifically for location information data can be applied to the travel industry. This allows for industry-specific data provision by applying different provision algorithms according to the company's industry.

[0138] The data delivery system can estimate the user's emotions and prioritize data delivery based on those emotions. For example, if the user is relaxed, detailed data delivery can be prioritized. If the user is stressed, simpler data delivery can be prioritized. Furthermore, if the user is in a hurry, rapid data delivery can be prioritized. By prioritizing data delivery based on the user's emotions, this system reduces the user's burden and delivers data efficiently.

[0139] The data provision system can prioritize providing highly relevant data based on a company's geographical location. For example, if a company needs data related to a specific region, it can prioritize providing data for that region. Similarly, if a company needs global data, it can provide data covering a wide range of areas. Furthermore, if a company needs data related to a specific city, it can prioritize providing data for that city. This allows for the efficient provision of region-specific data by prioritizing highly relevant data based on a company's geographical location.

[0140] The following briefly describes the processing flow for example form 2.

[0141] Step 1: The data collection unit collects data from the user, such as purchase history, location information, and health data. For example, purchase history includes the type of product purchased, the date and time of purchase, and the location of purchase; location information includes GPS data, Wi-Fi location information, and beacon data; and health data includes heart rate, steps taken, and sleep data. Step 2: The data protection unit securely manages the data collected by the collection unit. For example, it encrypts the collected data for secure management, implements access control, and performs regular data backups. Step 3: The data provision department provides the data managed by the data protection department to the company. For example, it provides data to the company in real time, provides data in a specified format, and sets the frequency of data provision. Step 4: The market research department uses the data provided by the data provision department to conduct market research and product development. For example, they analyze consumer behavior patterns based on the provided data, understand consumer needs, and identify consumer problems. Step 5: The Product Development Department improves product development and marketing strategies based on the results obtained by the Market Research Department. For example, they develop products, improve marketing strategies, and develop new products based on the analysis results. Step 6: The rewards department provides rewards to users based on the data provided by the data department. For example, it may provide points, coupons, or cash to users each time the data is used.

[0142] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0143] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0144] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0145] Each of the multiple elements described above, including the data collection unit, data protection unit, data provision unit, market research unit, product development unit, and reward provision unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the data collection unit is implemented by the computer 36 of the smart device 14 and collects the user's purchase history, location information, and health data. The data protection unit is implemented by the specific processing unit 290 of the data processing unit 12 and encrypts and securely manages the collected data. The data provision unit is implemented by the specific processing unit 290 of the data processing unit 12 and provides data to companies in real time. The market research unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes consumer behavior patterns based on the provided data. The product development unit is implemented by the specific processing unit 290 of the data processing unit 12 and improves product development and marketing strategies based on the analysis results. The reward provision unit is implemented by the control unit 46A of the smart device 14 and provides points to the user each time data is used. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0146] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0147] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0148] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0149] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0150] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0151] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0152] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0153] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0154] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0155] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0156] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0157] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0158] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0159] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0160] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0161] Each of the multiple elements described above, including the data collection unit, data protection unit, data provision unit, market research unit, product development unit, and reward provision unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the data collection unit is implemented by the computer 36 of the smart glasses 214 and collects the user's purchase history, location information, and health data. The data protection unit is implemented by the specific processing unit 290 of the data processing unit 12 and encrypts and securely manages the collected data. The data provision unit is implemented by the specific processing unit 290 of the data processing unit 12 and provides data to companies in real time. The market research unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes consumer behavior patterns based on the provided data. The product development unit is implemented by the specific processing unit 290 of the data processing unit 12 and improves product development and marketing strategies based on the analysis results. The reward provision unit is implemented by the control unit 46A of the smart glasses 214 and provides points to the user each time data is used. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0162] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0163] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0164] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0165] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0166] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0167] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0168] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0169] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0170] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0171] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0172] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0173] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0174] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0175] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0176] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0177] Each of the multiple elements described above, including the data collection unit, data protection unit, data provision unit, market research unit, product development unit, and reward provision unit, is implemented by, for example, at least one of the headset terminal 314 and the data processing unit 12. For example, the data collection unit is implemented by the computer 36 of the headset terminal 314 and collects the user's purchase history, location information, and health data. The data protection unit is implemented by the specific processing unit 290 of the data processing unit 12 and encrypts and securely manages the collected data. The data provision unit is implemented by the specific processing unit 290 of the data processing unit 12 and provides data to companies in real time. The market research unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes consumer behavior patterns based on the provided data. The product development unit is implemented by the specific processing unit 290 of the data processing unit 12 and improves product development and marketing strategies based on the analysis results. The reward provision unit is implemented by the control unit 46A of the headset terminal 314 and provides points to the user each time data is used. The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0178] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0179] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0180] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0181] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0182] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0183] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0184] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0185] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0186] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0187] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0188] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0189] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0190] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0191] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0192] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0193] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0194] Each of the multiple elements described above, including the data collection unit, data protection unit, data provision unit, market research unit, product development unit, and reward provision unit, is implemented by, for example, at least one of the robot 414 and the data processing unit 12. For example, the data collection unit is implemented by the computer 36 of the robot 414 and collects the user's purchase history, location information, and health data. The data protection unit is implemented by the specific processing unit 290 of the data processing unit 12 and encrypts and securely manages the collected data. The data provision unit is implemented by the specific processing unit 290 of the data processing unit 12 and provides data to companies in real time. The market research unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes consumer behavior patterns based on the provided data. The product development unit is implemented by the specific processing unit 290 of the data processing unit 12 and improves product development and marketing strategies based on the analysis results. The reward provision unit is implemented by the control unit 46A of the robot 414 and provides points to the user each time data is used. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0195] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0196] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0197] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0198] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0199] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0200] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0201] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0202] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0203] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0204] 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.

[0205] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0206] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0207] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0208] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0209] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0210] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0211] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0212] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0213] (Note 1) A data collection unit that collects data such as purchase history, location information, and health data from users, A data protection unit securely manages the data collected by the aforementioned collection unit, A data provision unit that provides data managed by the aforementioned data protection unit to companies, The Market Research Department utilizes the data provided by the aforementioned Data Provision Department to conduct market research and product development, The Product Development Department improves product development and marketing strategies based on the results obtained by the aforementioned Market Research Department, The system includes a reward provision unit that provides rewards to users based on the data provided by the data provision unit. A system characterized by the following features. (Note 2) The aforementioned collection unit is It collects user purchase history, location information, health data, etc. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned data protection unit Encrypt the collected data and manage it securely. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned data provision unit, Providing real-time data to businesses The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned market research department, We analyze consumer behavior patterns based on the data provided. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned product development department, We will improve product development and marketing strategies based on the analysis results. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned compensation provision unit, Points are awarded to the user each time data is used. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned market research department, We conduct surveys with users regarding their lifestyles and consumer behavior. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned product development department, We will improve product development and marketing strategies based on the survey results. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned collection unit is We estimate the user's emotions and adjust the timing of data collection based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned collection unit is Analyze the user's past data provision history and select the optimal collection method. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned collection unit is When collecting data, filtering is performed based on the user's current activities and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned collection unit is It estimates the user's emotions and prioritizes the data to collect based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned collection unit is During data collection, the system prioritizes the collection of highly relevant data based on the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned collection unit is During data collection, the system analyzes users' social media activity and collects relevant data. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned data protection unit It estimates the user's emotions and adjusts the level of data protection based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned data protection unit When protecting data, the encryption strength is adjusted based on the importance of the collected data. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned data protection unit When protecting data, different protection algorithms are applied depending on the data category. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned data protection unit It estimates user sentiment and determines data protection priorities based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned data protection unit When protecting data, prioritize protection based on when the data was submitted. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned data protection unit When protecting data, adjust the order of protection based on the relevance of the data. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned data provision unit, We estimate the user's emotions and adjust the timing of data delivery based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned data provision unit, When providing data, we adjust the level of detail provided based on the company's needs. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned data provision unit, When providing data, different data delivery algorithms are applied depending on the company's industry. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned data provision unit, We estimate user sentiment and determine the priority of data provision based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned data provision unit, When providing data, we prioritize providing highly relevant data based on the company's geographical location information. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned data provision unit, When providing data, we analyze the company's social media activities and provide relevant data. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned market research department, We estimate user sentiment and adjust market research standards based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned market research department, When conducting market research, consider the interrelationships between data to improve the accuracy of the research. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned market research department, When conducting market research, the data should be analyzed while considering the attributes of the data submitters. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned market research department, It estimates user sentiment and adjusts the order in which market research results are displayed based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 32) The aforementioned market research department, When conducting market research, consider the geographical distribution of the data. The system described in Appendix 1, characterized by the features described herein. (Note 33) The aforementioned market research department, When conducting market research, referencing relevant literature to improve the accuracy of the research. The system described in Appendix 1, characterized by the features described herein. (Note 34) The aforementioned product development department, We estimate user emotions and adjust product development methods based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 35) The aforementioned product development department, During product development, we analyze users' past consumer behavior to select the optimal development method. The system described in Appendix 1, characterized by the features described herein. (Note 36) The aforementioned product development department, During product development, customize the development process based on the user's current living situation. The system described in Appendix 1, characterized by the features described herein. (Note 37) The aforementioned product development department, We estimate user emotions and determine product development priorities based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 38) The aforementioned product development department, During product development, the optimal development method is selected based on the user's geographical location information. The system described in Appendix 1, characterized by the features described herein. (Note 39) The aforementioned product development department, During product development, we analyze users' social media activity and propose development methods. The system described in Appendix 1, characterized by the features described herein. (Note 40) The aforementioned compensation provision unit, The system estimates the user's emotions and adjusts the reward system based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 41) The aforementioned compensation provision unit, When providing rewards, adjust the level of detail of the rewards based on how often the data is used. The system described in Appendix 1, characterized by the features described herein. (Note 42) The aforementioned compensation provision unit, When providing rewards, different reward algorithms are applied depending on the data category. The system described in Appendix 1, characterized by the features described herein. (Note 43) The aforementioned compensation provision unit, The system estimates the user's emotions and determines the priority of reward provision based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 44) The aforementioned compensation provision unit, When providing rewards, we prioritize rewards based on when the data was used. The system described in Appendix 1, characterized by the features described herein. (Note 45) The aforementioned compensation provision unit, When providing rewards, the order of rewards will be adjusted based on the relevance of the data. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0214] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A data collection unit that collects data such as purchase history, location information, and health data from users, A data protection unit securely manages the data collected by the aforementioned collection unit, A data provision unit that provides data managed by the aforementioned data protection unit to companies, The Market Research Department utilizes the data provided by the aforementioned Data Provision Department to conduct market research and product development, The Product Development Department improves product development and marketing strategies based on the results obtained by the aforementioned Market Research Department, The system includes a reward provision unit that provides rewards to users based on the data provided by the data provision unit. A system characterized by the following features.

2. The aforementioned collection unit is It collects user purchase history, location information, health data, etc. The system according to feature 1.

3. The aforementioned data protection unit Encrypt the collected data and manage it securely. The system according to feature 1.

4. The aforementioned data provision unit, Providing real-time data to businesses The system according to feature 1.

5. The aforementioned market research department, We analyze consumer behavior patterns based on the data provided. The system according to feature 1.

6. The aforementioned product development department, We will improve product development and marketing strategies based on the analysis results. The system according to feature 1.

7. The aforementioned compensation provision unit, Points are awarded to the user each time data is used. The system according to feature 1.

8. The aforementioned market research department, We conduct surveys with users regarding their lifestyles and consumer behavior. The system according to feature 1.

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