system

Abstract

We provide the system. [Solution] A means of obtaining location information from a data provisioning terminal, A means of integrating acquired location information and removing duplicate or incomplete data, A means of making integrated location information tradable in the market, A system that includes a means of distributing revenue to data providers according to the transaction amount.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] Modern generative AI models have a problem in that it is difficult to utilize real and vast data in economic activities conducted outside the Internet. In particular, due to the lack of location information, offline purchase data, and real-world data related to events, the performance of generative AI models is limited. There is a need for means to eliminate such data deficiencies and quickly and efficiently collect and monetize diverse data.

Means for Solving the Problems

[0005] This invention provides a means for acquiring location information from data-providing terminals, integrating that information, and removing duplicate and incomplete data. It also provides a system that makes the integrated location information tradable in the market and distributes revenue to data providers according to the transaction amount. Furthermore, by storing the location information in a time-series database, it enables the generation of custom datasets based on the request conditions of data buyers. This allows for the efficient collection and utilization of diverse real-world data necessary for generating AI models.

[0006] A "data provision terminal" refers to a device or application that collects location information and transmits it to a server.

[0007] "Location information" refers to data that indicates the geographical location of a specific device or user.

[0008] "Integration" refers to the process of combining data acquired from different devices and sources and converting it into a standard format.

[0009] "Duplicate data" refers to data that contains the same information multiple times.

[0010] "Incomplete data" refers to data that is missing necessary information.

[0011] "Making data tradable on the market" means making the integrated data available for purchase by third parties.

[0012] "Distributing profits" refers to allocating the revenue earned to stakeholders based on predetermined criteria.

[0013] A "time-series database" refers to a database used to manage data that changes over time.

[0014] A "custom dataset" refers to a dataset created for a specific purpose based on certain conditions. [Brief explanation of the drawing]

[0015] [Figure 1] It is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] It is a conceptual diagram showing an example of the main functions of a data processing device and a smart device according to the first embodiment. [Figure 3] It is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] It 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] It is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] It 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] It is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] It 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] It shows an emotion map to which a plurality of emotions are mapped. [Figure 10] It shows an emotion map to which a plurality of emotions are mapped. [Figure 11] It is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] It is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] It is a sequence diagram showing the processing flow of the data processing system in Example 2 when an emotion engine is combined. [Figure 14] It is a sequence diagram showing the processing flow of the data processing system in Application Example 2 when an emotion engine is combined.

Embodiments for Carrying Out the Invention

[0016] Hereinafter, an example of an embodiment of a system according to the technology of the present disclosure will be described with reference to the accompanying drawings.

[0017] First, the terms used in the following description will be explained.

[0018] In the following embodiments, the numbered processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Also, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), and the like.

[0019] In the following embodiments, the numbered RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor.

[0020] In the following embodiments, the numbered storage is one or more non-volatile storage devices that store various programs and various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes, and the like.

[0021] In the following embodiments, the signed communication interface (I / F) is an interface that includes a communication processor and an antenna, etc. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0022] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." That is, "A and / or B" means that it may be A alone, or B alone, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" applies when expressing three or more things linked by "and / or."

[0023] [First Embodiment]

[0024] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0025] As shown in Figure 1, the data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0026] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0027] The smart device 14 comprises a computer 36, a reception device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The reception device 38, output device 40, and camera 42 are also connected to the bus 52.

[0028] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, etc., and receives user input. The touch panel 38A receives user input by detecting contact with an object (e.g., a pen or finger). The microphone 38B receives user input by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0029] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form perceptible to the user 20 (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0030] 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.

[0031] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0032] 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.

[0033] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0034] In the smart device 14, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The reception output program 60 is used in conjunction with a specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0035] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0036] This invention is a system for effectively collecting real-world data and facilitating the development of generative AI models. Specific embodiments for carrying out this invention are described in detail below.

[0037] System Configuration

[0038] 1. Data collection by devices

[0039] Users grant permission to share their location information on their smartphones and other mobile devices. The device periodically collects location information with the user's permission, using GPS and Wi-Fi data. The collected data is encrypted and sent to the server.

[0040] 2. Data integration and processing by the server

[0041] The server receives and analyzes location information transmitted from multiple terminals. The data is stored in a time-series database, and duplicates and incomplete data are removed. Data integration creates a clean dataset in a standardized format.

[0042] 3. Buying, selling, and providing data on the marketplace.

[0043] Users (data buyers) can access the platform online and search for datasets that meet their needs. The data is provided in custom formats filtered by different criteria. The datasets selected by buyers are traded through secure online payments.

[0044] 4. Profit sharing

[0045] The server distributes revenue to data providers based on the sales generated. The distribution is calculated based on the volume and frequency of data use and transferred to the provider's registered financial institution account. The revenue structure is designed to ensure fair distribution to each data provider.

[0046] Specific example

[0047] Collection and transmission of location information

[0048] When a device visits a store, its location information is transmitted to the server in real time. The frequency of data collection by the device can be adjusted in the application settings.

[0049] Customizing and selling datasets

[0050] In a data marketplace provided by a server, if a retailer requests customer visit patterns for a specific metropolitan area, a dataset tailored to those conditions is generated and provided.

[0051] This invention enables the efficient use of location information and other real-world data, allowing for the collection of large amounts of data that form the basis for AI model development. Through this system, a platform is realized that benefits both data providers and buyers.

[0052] The following describes the processing flow.

[0053] Step 1:

[0054] The device collects location data from devices that have been authorized to share their location. Location information is acquired periodically and its accuracy is maintained based on GPS signals and Wi-Fi networks.

[0055] Step 2:

[0056] The device converts the collected location information into data packets, encrypts them to ensure security, and then transmits them to the server via the internet.

[0057] Step 3:

[0058] The server analyzes the received location data and stores it in a time-series database. The data is formatted to a standardized format, and duplicate or incomplete data is removed using algorithms.

[0059] Step 4:

[0060] The server prepares the integrated dataset for listing on the data marketplace. The data is tagged so that it can be filtered by various metrics.

[0061] Step 5:

[0062] Users (data buyers) access the data marketplace and search for datasets that meet specific criteria. The selected data is then customized to the buyer's needs.

[0063] Step 6:

[0064] The server provides the data set selected by the buyer through a secure payment system. Once payment is complete, the data is transferred to the buyer.

[0065] Step 7:

[0066] The server calculates revenue for data providers based on data transaction information. It automatically transfers the revenue-sharing payments to registered bank accounts.

[0067] (Example 1)

[0068] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0069] Conventional location data collection systems have been inefficient in collecting data from users, and processes for encryption and ensuring data reliability have been inadequate. Furthermore, in the data marketplace, the processes of providing and purchasing data have not adequately generated customized datasets that meet user needs or ensured fair revenue sharing. There is a need to solve these problems and provide a system that efficiently and securely collects, processes, and trades location data.

[0070] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0071] In this invention, the server includes means for encrypting location information and transmitting it to the server using a secure communication protocol, means for storing the decrypted location information in a time-series data store, and means for analyzing the integrated location information, removing duplicate and incomplete data, and generating a clean dataset in a standardized format. This enables efficient and secure collection of location information from users, highly reliable data processing, and rapid data provision to the market.

[0072] A "data provision terminal" is an electronic device used to acquire location information from a user and transmit it to a server.

[0073] "Location information" refers to information about the user's current location obtained by the data provision terminal.

[0074] "Encryption" is the process of transforming data into a format that cannot be read by third parties in order to protect the confidentiality of the data.

[0075] A "secure communication protocol" is a network communication method designed to protect the confidentiality and integrity of data during transmission.

[0076] A "server" is a central computing device that receives data sent from terminals, processes it, and stores and manages it.

[0077] "Decryption" is the process of returning encrypted data to its original, readable format.

[0078] A "time-series data store" is a data management system for organizing and storing data based on time.

[0079] "Duplicate data" refers to information within a dataset where the same content is recorded multiple times.

[0080] "Incomplete data" refers to data that lacks necessary information, making it impossible to perform sufficient judgments or processing.

[0081] A "standardized format" is a set of criteria for organizing data recorded in different formats or units into a unified form.

[0082] A "clean dataset" is a collection of consistent and accurate data from which duplicates and errors have been removed.

[0083] A "data marketplace" is an online platform where data providers sell their data, and data buyers purchase and use that data.

[0084] A "custom dataset" is a collection of data that has been processed based on specific conditions or needs.

[0085] "Revenue sharing" is the process of fairly distributing the profits earned from data trading to data providers.

[0086] This invention provides a system that enables the efficient and secure collection, processing, and trading of location data. This system includes the following main components:

[0087] 1. Collection of location information by the device

[0088] Users grant permission to share their location information on their smartphones and other mobile devices. With the user's consent, the device periodically acquires location information using GPS and Wi-Fi signals. This allows for real-time tracking of the user's movements and current location. Furthermore, the location information is immediately encrypted using the AES encryption algorithm, ensuring the security of the information.

[0089] 2. Encryption and Data Transmission

[0090] The device sends encrypted location information to the server using a secure communication protocol (e.g., HTTPS). This process reduces the risk of eavesdropping or tampering during data transmission.

[0091] 3. Receiving and processing data by the server

[0092] The server decrypts the encrypted data received from the terminal and stores it in a time-series data store. This data store maintains data integrity and appropriately removes duplicate and incomplete data. As a result, a standardized, clean dataset is generated.

[0093] 4. Trading in the data market

[0094] Users (data buyers) can search for datasets tailored to their needs through an online platform. The server generates custom datasets in response to the data buyer's request and provides the necessary data. Buyers can purchase datasets through a secure online payment system and access them immediately. A specific example of a prompt might be, "I want to analyze the weekly visit patterns of visitors in a specific region. Please provide a suitable dataset."

[0095] This system creates an innovative platform that benefits both data providers and buyers, effectively providing the data infrastructure necessary for developing generative AI models.

[0096] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0097] Step 1:

[0098] The device obtains permission from the user to share its location. Upon receiving this input, the device acquires the user's location information using GPS or Wi-Fi. This location information is displayed on the electronic device in the form of latitude and longitude. The device then encrypts the acquired location information using the AES encryption algorithm. This encrypted data becomes the output.

[0099] Step 2:

[0100] The device sends encrypted location information to the server using a secure communication protocol (such as HTTPS). Using this protocol reduces the risk of third-party interception of the data during transmission. The output confirms that the encrypted data has safely reached the server.

[0101] Step 3:

[0102] The server receives encrypted data from the terminal as input. The server uses the decryption key to decrypt the data and restore it to its original location information. This decrypted location information is stored in a time-series data store. Here, duplicate and missing data are checked, and a clean dataset is output.

[0103] Step 4:

[0104] The server processes a clean dataset into a format suitable for the data market. Based on the request criteria from data buyers, it generates custom datasets filtered according to specific conditions. During this process, the dataset is evaluated to ensure it accurately matches the buyer's requirements, and a customized dataset is generated as output.

[0105] Step 5:

[0106] Users (data buyers) search for and purchase custom datasets on an online platform. Payment is made using a secure online payment system based on the user's selected dataset. An accessible download link is generated immediately after the purchase is complete.

[0107] Step 6:

[0108] The server calculates the transaction value obtained from the sale of datasets. Based on this transaction value, it distributes revenue to data providers. Revenue is calculated considering the amount of data provided and the frequency of use, and is transferred to the data provider's registered financial institution account. As an output, a fair revenue distribution is implemented.

[0109] (Application Example 1)

[0110] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0111] In large facilities such as logistics centers, there is a need to optimize the movement of mobile equipment such as robots and to manage them efficiently. However, currently, highly accurate optimization based on sufficient information is not being performed, which can lead to logistics congestion and unexpected delays. A system is needed to resolve this problem.

[0112] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0113] In this invention, the server includes a device for acquiring location information, a device for integrating the acquired location information and removing duplicate or incomplete data, and a device for optimizing logistics management using a generative AI model. This enables efficient optimization of the movement of mobile equipment in a logistics center and highly accurate logistics management.

[0114] A "location information acquisition device" is a device that has the function of detecting the user's location using GPS or Wi-Fi data in order to identify the user's travel route and place of stay.

[0115] A "device that integrates acquired location information and removes duplicate and incomplete data" is a device that aggregates location information obtained from multiple data sources and has the function of eliminating duplicate information and incomplete data in order to generate a consistent and clean dataset.

[0116] A "device for optimizing logistics management using generative AI models" is a system that generates algorithms and predictive models to optimize the movement and work efficiency of equipment and robots within a logistics center, based on aggregated location information.

[0117] A "device for distributing revenue to data providers according to transaction amount" is a system that has the function of distributing revenue obtained from selling acquired data in proportion to the transaction amount, in order to fairly return that revenue to data providers.

[0118] This invention is a system that links a server and terminals to optimize the movement of mobile equipment in a logistics center. The server is primarily responsible for data aggregation and analysis, while the terminals are responsible for collecting location information. Details of the embodiment are shown below.

[0119] The device periodically collects location information as the user or device moves. This location information is obtained using a GPS module or Wi-Fi, encrypted within the device, and sent to the server. The server receives the location information using a Python program or HTTP request library and records it in a database as real-time time-series data.

[0120] The server analyzes the received location information and removes duplicate and incomplete data. Advanced data processing techniques are used to ensure clean data in order to generate a standardized dataset. Furthermore, a generative AI model is used to predict patterns for optimizing movement routes within the logistics center, supporting route suggestions and efficiency improvements.

[0121] As a concrete example, consider an operational scenario in a large logistics center. By utilizing this system when logistics robots move goods at various locations, it becomes possible to avoid congested areas and set the most efficient routes. This improves delivery speed and increases the volume of goods handled per hour.

[0122] Example of a prompt:

[0123] "Please explain the method for generating models using location data to optimize robot movement within a logistics center."

[0124] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0125] Step 1:

[0126] The device acquires location information. When user movement is detected, the device uses its GPS module and Wi-Fi to obtain its current location. This information includes location coordinates and a timestamp. The acquired data is encrypted and prepared to be sent to the server.

[0127] Step 2:

[0128] The server receives location information. The server receives encrypted location data sent from the terminal and stores it in its database. At this time, the received data is recorded in real time and enters a waiting state for subsequent data processing.

[0129] Step 3:

[0130] The server performs data cleansing. It analyzes the received location data and removes duplicate and incomplete data. A data duplication checking algorithm is used to create a clean and consistent dataset. The output is an optimized standard dataset.

[0131] Step 4:

[0132] The server applies a generated AI model. The generated AI model takes a clean dataset as input and generates a model that predicts efficient robot movement within the logistics center. This allows for real-time proposal of the most likely optimal route, thereby improving movement efficiency.

[0133] Step 5:

[0134] The user receives optimized data and suggestions. The server presents the user with predicted optimal route information, which is then used in actual operations within the logistics center. Based on the information provided, the user adjusts the movement of robots and personnel to achieve efficient logistics management.

[0135] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0136] This invention is a system that recognizes user emotions and combines that data with location information to provide a richer dataset. This system enables the collection, integration, analysis, and provision of various types of data, and facilitates the development and utilization of generative AI models.

[0137] System Configuration

[0138] 1. Collection of emotional data and location information by devices

[0139] Users install an application that enables emotion recognition using their smartphones or wearable devices. The device analyzes the subject's voice, facial expressions, and biosignals, and acquires emotion data using an emotion engine. Simultaneously, it collects location information, packets both types of data, and sends them to the server.

[0140] 2. Data integration and processing by the server

[0141] The server stores sentiment data and location information received from multiple terminals in a time-series database. During integration, it detects data duplication and missing data and processes it to ensure it is stored in a standardized format. It also evaluates the relationship between sentiment data and location information and constructs the necessary datasets.

[0142] 3. Sales and provision of data on the market

[0143] Users (data buyers) can search for datasets related to specific emotional states or geographical locations through the online platform. Customized datasets, tailored to the buyer's requirements, are provided in an appropriate format and delivered via secure payment.

[0144] 4. Profit sharing

[0145] The server distributes revenue to data providers who provided emotional data and location information, based on the sales generated. Revenue is calculated and distributed fairly based on the utilization value of the data.

[0146] Specific example

[0147] Acquisition and utilization of emotional data

[0148] The device recognizes the user's emotions, such as joy or sadness, and also acquires their location information at that time. This data is used by marketing companies to analyze the effectiveness of advertisements.

[0149] Providing custom datasets

[0150] In a server-based service, if a retailer wants to know the sentiment trends of consumers in a specific area, the server generates and provides a dataset based on those conditions.

[0151] This invention maximizes the impact of location information with added emotional data on the generated AI model, enabling the provision of new business insights. Through this system, more advanced and context-sensitive data utilization is expected.

[0152] The following describes the processing flow.

[0153] Step 1:

[0154] The device uses an emotion recognition application installed on the user's device to acquire emotional data from the user's voice, facial expressions, and biosignals. The emotion engine analyzes this data in real time to identify the emotional state.

[0155] Step 2:

[0156] The device acquires emotional data and simultaneously collects current location information using its GPS function. The emotional data and location information are paired and encrypted as a single data packet.

[0157] Step 3:

[0158] The device sends encrypted data packets to the server via the internet connection specified by the user. This transmission uses a protocol (e.g., HTTPS) that protects data accuracy and privacy.

[0159] Step 4:

[0160] The server analyzes the received data packets and stores sentiment data and location information in a time-series database. The data integration process removes duplicate data and detects and corrects missing data.

[0161] Step 5:

[0162] The server generates custom datasets linked to sentiment and location based on requests from buyers who use the data. This process involves filtering and aggregation to address specific marketing needs and analytical requests.

[0163] Step 6:

[0164] Users (data buyers) access the data through an online platform, select and purchase the custom datasets they need. The server conducts the transaction through a secure payment system and delivers the data to the buyer.

[0165] Step 7:

[0166] The server distributes revenue to data providers. It calculates the distribution based on the transaction amount and automatically transfers it to the data provider's registered account. This process is conducted fairly and transparently.

[0167] (Example 2)

[0168] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0169] In modern society, the importance of effectively integrating people's emotions and location information for commercial and research purposes is increasing. However, challenges remain, including inefficiencies from processing emotional data and geographical information separately, the problem of duplicate data, and the difficulty of fairly distributing revenue to data providers. This makes it difficult to accurately utilize data and maintain the motivation of data providers.

[0170] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0171] In this invention, the server includes means for acquiring emotional data and location data from users, means for integrating the acquired emotional data and location data to remove duplicate and incomplete data, and means for making the integrated data available for development and utilization of generating AI models. This enables the effective integration of emotional information and location information to enhance commercial value while simultaneously allowing for fair revenue sharing to data providers.

[0172] A "user" refers to a person who provides emotional data and location data using this system.

[0173] "Emotional data" refers to information about a user's emotions obtained by analyzing their voice, facial expressions, and biosignals.

[0174] "Location data" refers to data that indicates a user's geographical location, and primarily refers to information obtained using sensor technologies such as GPS.

[0175] A "server" refers to a centralized computer system that receives, integrates, stores, analyzes, and provides data sent from terminals.

[0176] A "generative AI model" refers to an intelligent model developed using machine learning techniques to assist human tasks.

[0177] "Integration method" refers to the process by which the server combines the emotional and location data it receives, eliminating duplication and missing data, and converts it into a format that is easy to analyze.

[0178] A "revenue sharing mechanism" refers to a calculation and payment system that allows a server to fairly distribute revenue to data providers based on the amount of data traded.

[0179] A "dataset" refers to a collection of data organized based on specific criteria and used for analysis or trading.

[0180] This invention is a system that collects and integrates user emotion data and location data to provide it as a marketable dataset. Users install an emotion recognition application on a device such as a smartphone or wearable device. This application is designed using AI technology and is capable of voice recognition, facial recognition, and biosignal analysis. The device collects emotion data by analyzing the user's voice, facial expressions, and biosignals. Simultaneously, it acquires accurate location data using a GPS sensor.

[0181] The emotional and location data collected by the device are combined into a single packet and sent to the server using a secure communication protocol. The server stores the received data in a time-series database and performs integration and analysis. While checking for data duplication and missing information, it utilizes a generative AI model to evaluate the relationship between emotional and location information and generates a dataset that can be used for commercial and research purposes.

[0182] One concrete example of using this system is for marketing purposes. For instance, if a company is running an advertising campaign for a new product and wants to understand the emotional response of consumers in a specific region, the server can provide a dataset that matches those criteria.

[0183] Possible prompts for the generative AI model include: "Generate a dataset of users who are happy in a specific region and analyze how to maximize the effectiveness of advertising campaigns."

[0184] This system makes it possible to gain new business insights by effectively utilizing location data with added emotional information. A mechanism for fair and effective revenue sharing with data providers is also incorporated.

[0185] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0186] Step 1:

[0187] The user installs an emotion recognition app on their smartphone or wearable device. When the app is launched, it receives the user's voice, facial expressions, and biosignals as input data. The device analyzes this data using an AI model and outputs emotion data that identifies the user's emotional state.

[0188] Step 2:

[0189] The device simultaneously uses a GPS module to obtain precise location data. This location data indicates the user's current location and is packetized together with sentiment data. This packet contains information about the user's location and the emotions they are experiencing.

[0190] Step 3:

[0191] The device transmits acquired emotion and location data to the server via a secure communication protocol. The input here is data packets from the device, which the server receives and stores in time-series data storage.

[0192] Step 4:

[0193] The server analyzes the received data packets and automatically cleans up duplicate and incomplete data. The data processing performed by the server includes filtering to improve data accuracy. The output is a unified, cleaned dataset.

[0194] Step 5:

[0195] The server uses a generative AI model to evaluate the relationship between sentiment data and location data. This processing step involves analyzing a cleaned dataset as input and generating a commercially usable dataset as output.

[0196] Step 6:

[0197] The server provides customized datasets to data buyers. When a user requests a specific dataset through the online platform, the server searches for data that matches the criteria and outputs it in the appropriate format.

[0198] Step 7:

[0199] The server distributes the revenue generated from the sale of data to the data providers. In this step, transaction revenue is used as input, and a fair revenue distribution is performed based on a pre-configured algorithm. The output is the fairly calculated reward.

[0200] (Application Example 2)

[0201] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as a "server" and the smart device 14 as a "terminal".

[0202] Traditional advertising delivery systems have the challenge of not adequately providing personalized advertising experiences that respond to users' emotions and circumstances. Furthermore, it has been difficult to efficiently integrate emotional data and location information and provide it as marketable data.

[0203] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0204] In this invention, the server includes means for acquiring emotional data from users, means for acquiring location information from data provision terminals, means for integrating the acquired emotional data and location information to remove duplicate and incomplete data, means for generating marketable information based on the integrated emotional data and location information, and means for presenting personalized data according to the conditions of the information purchaser. This makes it possible to provide an advertising experience that is tailored to the user's emotions and circumstances.

[0205] "Emotional data" refers to data acquired to represent a user's emotional state, and is obtained by analyzing voice, facial expressions, and biosignals.

[0206] "Location information" refers to geographical data used to determine the current location of a device, and is obtained using methods such as GPS.

[0207] "Integration" refers to the process of combining information obtained from multiple data sources into a single, standardized format, removing redundancies and missing data.

[0208] "Personalized data" refers to datasets that have been customized according to the specific conditions and requirements of the information purchaser.

[0209] "Marketable information" refers to information that integrates acquired sentiment data and location information in a commercially valuable form, making it available for buying and selling on the data market.

[0210] "Providing an advertising experience" means presenting the most relevant advertisements to users based on their emotional state and location information.

[0211] To implement this invention, a system is needed to collect user emotion data and integrate it with location information. Specifically, terminals such as smartphones and wearable devices are used, and emotion recognition applications are installed on these terminals. The emotion recognition application analyzes voice, facial expressions, and biosignals to generate user emotion data. Location information is obtained using GPS.

[0212] The device transmits acquired emotion data and location information to the server. The server integrates this data using the Google® Maps API and time-series databases. It checks for duplicate or missing data, and stores it in a standardized format. Using a generative AI model, it selects the most suitable advertisements for the user's situation from the integrated data.

[0213] The server delivers personalized advertisements to users in real time through the ad delivery network. For example, if a user expresses joy while in a specific commercial area, it can instantly deliver advertisements to that user that include discount information for nearby stores.

[0214] As a concrete example, the prompt "Select the most suitable advertisement when a user expresses surprise in a specific area. For example, consider a special invitation advertisement for a nearby new attraction." is used to perform data analysis and ad selection by a generative AI model.

[0215] This makes it possible to provide personalized advertising experiences that utilize emotional data and location information, enabling the delivery of information tailored to the user's situation.

[0216] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0217] Step 1:

[0218] The device analyzes the user's voice, facial expressions, and biosignals using an emotion recognition application to acquire emotional data. Inputs include voice, facial images, and biosignals, which are processed by an emotion analysis algorithm to output quantitative data representing the user's emotions, such as joy or surprise.

[0219] Step 2:

[0220] The device acquires location information from its GPS sensor along with the acquired emotional data. It receives emotional data and geographical coordinates as input, packets them into an integrated dataset, and sends it to the server. This integrated data links the user's emotional state with their location at that time.

[0221] Step 3:

[0222] The server uses the Google Maps API to match received sentiment data and location information with map data and stores it in a time-series database. The input consists of sentiment data and location information, which are converted into corresponding geographical information, checked for duplicates and missing data, and then output in a standardized format.

[0223] Step 4:

[0224] The server uses a generative AI model to select the most suitable advertisement based on the user's current situation from integrated data. Input consists of user data and prompt messages stored in a time-series database, and individual advertisement selection is performed using a machine learning algorithm. The output is the selected advertisement information.

[0225] Step 5:

[0226] The server sends selected advertisements to the device and displays them to the user. It utilizes an ad delivery network to provide real-time advertising. The input is advertising information, and the output is the display of advertisements on the user's device screen.

[0227] Step 6:

[0228] When a user responds to an advertisement (by clicking or taking action), the server collects this data and uses it to analyze the effectiveness of the advertisement and improve future selection algorithms. The input is user action data, and the output is the results of the advertisement effectiveness analysis.

[0229] 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.

[0230] Data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0231] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart device 14.

[0232] [Second Embodiment]

[0233] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0234] 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.

[0235] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0236] 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.

[0237] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0238] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0239] 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.

[0240] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0241] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0242] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0243] In the smart glasses 214, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0244] Next, the identification processing performed by the identification processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0245] This invention is a system for effectively collecting real-world data and facilitating the development of generative AI models. Specific embodiments for carrying out this invention are described in detail below.

[0246] System Configuration

[0247] 1. Data collection by devices

[0248] Users grant permission to share their location information on their smartphones and other mobile devices. The device periodically collects location information with the user's permission, using GPS and Wi-Fi data. The collected data is encrypted and sent to the server.

[0249] 2. Data integration and processing by the server

[0250] The server receives and analyzes location information transmitted from multiple terminals. The data is stored in a time-series database, and duplicates and incomplete data are removed. Data integration creates a clean dataset in a standardized format.

[0251] 3. Buying, selling, and providing data on the marketplace.

[0252] Users (data buyers) can access the platform online and search for datasets that meet their needs. The data is provided in custom formats filtered by different criteria. The datasets selected by buyers are traded through secure online payments.

[0253] 4. Profit sharing

[0254] The server distributes revenue to data providers based on the sales generated. The distribution is calculated based on the volume and frequency of data use and transferred to the provider's registered financial institution account. The revenue structure is designed to ensure fair distribution to each data provider.

[0255] Specific example

[0256] Location information collection and transmission

[0257] When a device visits a store, its location information is transmitted to the server in real time. The frequency of data collection by the device can be adjusted in the application settings.

[0258] Customizing and selling datasets

[0259] In a data marketplace provided by a server, if a retailer requests customer visit patterns for a specific metropolitan area, a dataset tailored to those conditions is generated and provided.

[0260] This invention enables the efficient use of location information and other real-world data, allowing for the collection of large amounts of data that form the basis for AI model development. Through this system, a platform is realized that benefits both data providers and buyers.

[0261] The following describes the processing flow.

[0262] Step 1:

[0263] The device collects location data from devices that have been authorized to share their location. Location information is acquired periodically and its accuracy is maintained based on GPS signals and Wi-Fi networks.

[0264] Step 2:

[0265] The device converts the collected location information into data packets, encrypts them to ensure security, and then transmits them to the server via the internet.

[0266] Step 3:

[0267] The server analyzes the received location data and stores it in a time-series database. The data is formatted to a standardized format, and duplicate or incomplete data is removed using algorithms.

[0268] Step 4:

[0269] The server prepares the integrated dataset for listing on the data marketplace. The data is tagged so that it can be filtered by various metrics.

[0270] Step 5:

[0271] Users (data buyers) access the data marketplace and search for datasets that meet specific criteria. The selected data is then customized to the buyer's needs.

[0272] Step 6:

[0273] The server provides the data set selected by the buyer through a secure payment system. Once payment is complete, the data is transferred to the buyer.

[0274] Step 7:

[0275] The server calculates revenue for data providers based on data transaction information. It automatically transfers the revenue-sharing payments to registered bank accounts.

[0276] (Example 1)

[0277] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0278] Conventional location data collection systems have been inefficient in collecting data from users, and processes for encryption and ensuring data reliability have been inadequate. Furthermore, in the data marketplace, the processes of providing and purchasing data have not adequately generated customized datasets that meet user needs or ensured fair revenue sharing. There is a need to solve these problems and provide a system that efficiently and securely collects, processes, and trades location data.

[0279] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0280] In this invention, the server includes means for encrypting location information and transmitting it to the server using a secure communication protocol, means for storing the decrypted location information in a time-series based data store, and means for analyzing the integrated location information, removing duplicate data and incomplete data, and generating a clean data set in a standardized format. This enables efficient and secure collection of location information from users, high-reliability data processing, and rapid data provision in the market.

[0281] The "data providing terminal" is an electronic device for acquiring location information from a user and transmitting it to the server.

[0282] "Location information" is information regarding the current location of a user acquired by the data providing terminal.

[0283] ""Encryption"" is a process of converting data into a form that cannot be read by a third party in order to protect the confidentiality of the data.

[0284] A ""secure communication protocol"" is a network communication method for protecting the confidentiality and integrity of data during data transmission.

[0285] The ""server"" is a central computing device for receiving data transmitted from a terminal, processing it, and storing and managing it.

[0286] ""Decryption"" is a process of restoring encrypted data to its original readable form.

[0287] A ""time-series data store"" is a data management system for organizing and storing data based on time.

[0288] ""Duplicate data"" is information within a data set in which data having the same content is recorded multiple times.

[0289] ""Incomplete data"" is data that lacks necessary information and thus cannot be sufficiently judged or processed.

[0290] A "standardized format" is a set of criteria for organizing data recorded in different formats or units into a unified form.

[0291] A "clean dataset" is a collection of consistent and accurate data from which duplicates and errors have been removed.

[0292] A "data marketplace" is an online platform where data providers sell their data, and data buyers purchase and use that data.

[0293] A "custom dataset" is a collection of data that has been processed based on specific conditions or needs.

[0294] "Revenue sharing" is the process of fairly distributing the profits earned from data trading to data providers.

[0295] This invention provides a system that enables the efficient and secure collection, processing, and trading of location data. This system includes the following main components:

[0296] 1. Collection of location information by the device

[0297] Users grant permission to share their location information on their smartphones and other mobile devices. With the user's consent, the device periodically acquires location information using GPS and Wi-Fi signals. This allows for real-time tracking of the user's movements and current location. Furthermore, the location information is immediately encrypted using the AES encryption algorithm, ensuring the security of the information.

[0298] 2. Encryption and Data Transmission

[0299] The device sends encrypted location information to the server using a secure communication protocol (e.g., HTTPS). This process reduces the risk of eavesdropping or tampering during data transmission.

[0300] 3. Data Reception and Processing by the Server

[0301] The server decrypts the encrypted data received from the terminal and stores it in a time - series - based data store. In this data store, data integrity is maintained, and duplicate data and incomplete data are appropriately removed. As a result, a standardized clean data set is generated.

[0302] 4. Transactions in the Data Market

[0303] The user (data purchaser) can search for a data set that meets their needs via an online platform. The server generates a custom data set according to the requirements of the data purchaser and provides the necessary data. The purchaser can purchase the data set through a secure online payment system and access it immediately. Specifically, a prompt sentence such as "I want to analyze the weekly store visit patterns of visitors in a specific region. Please provide an appropriate data set." can be cited as an example.

[0304] This system realizes an innovative platform that benefits both data providers and purchasers, and effectively provides the data foundation necessary for the development of generative AI models.

[0305] The flow of the specific process in Example 1 will be described using FIG. 11.

[0306] Step 1:

[0307] The terminal obtains permission from the user to share location information. The terminal receives this input and obtains the user's location information using GPS or Wi - Fi. This location information is displayed on the electronic device in the form of latitude and longitude. The terminal encrypts the obtained location information using the AES encryption algorithm. This encrypted data is the output.

[0308] Step 2:

[0309] The device sends encrypted location information to the server using a secure communication protocol (such as HTTPS). Using this protocol reduces the risk of third-party interception of the data during transmission. The output confirms that the encrypted data has safely reached the server.

[0310] Step 3:

[0311] The server receives encrypted data from the terminal as input. The server uses the decryption key to decrypt the data and restore it to its original location information. This decrypted location information is stored in a time-series data store. Here, duplicate and missing data are checked, and a clean dataset is output.

[0312] Step 4:

[0313] The server processes a clean dataset into a format suitable for the data market. Based on the request criteria from data buyers, it generates custom datasets filtered according to specific conditions. During this process, the dataset is evaluated to ensure it accurately matches the buyer's requirements, and a customized dataset is generated as output.

[0314] Step 5:

[0315] Users (data buyers) search for and purchase custom datasets on an online platform. Payment is made using a secure online payment system based on the user's selected dataset. An accessible download link is generated immediately after the purchase is complete.

[0316] Step 6:

[0317] The server calculates the transaction value obtained from the sale of datasets. Based on this transaction value, it distributes revenue to data providers. Revenue is calculated considering the amount of data provided and the frequency of use, and is transferred to the data provider's registered financial institution account. As an output, a fair revenue distribution is implemented.

[0318] (Application Example 1)

[0319] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0320] In large facilities such as logistics centers, there is a need to optimize the movement of mobile equipment such as robots and to manage them efficiently. However, currently, highly accurate optimization based on sufficient information is not being performed, which can lead to logistics congestion and unexpected delays. A system is needed to resolve this problem.

[0321] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0322] In this invention, the server includes a device for acquiring location information, a device for integrating the acquired location information and removing duplicate or incomplete data, and a device for optimizing logistics management using a generative AI model. This enables efficient optimization of the movement of mobile equipment in a logistics center and highly accurate logistics management.

[0323] A "location information acquisition device" is a device that has the function of detecting the user's location using GPS or Wi-Fi data in order to identify the user's travel route and place of stay.

[0324] A "device that integrates acquired location information and removes duplicate and incomplete data" is a device that aggregates location information obtained from multiple data sources and has the function of eliminating duplicate information and incomplete data in order to generate a consistent and clean dataset.

[0325] A "device for optimizing logistics management using generative AI models" is a system that generates algorithms and predictive models to optimize the movement and work efficiency of equipment and robots within a logistics center, based on aggregated location information.

[0326] A "device for distributing revenue to data providers according to transaction amount" is a system that has the function of distributing revenue obtained from selling acquired data in proportion to the transaction amount, in order to fairly return that revenue to data providers.

[0327] This invention is a system that links a server and terminals to optimize the movement of mobile equipment in a logistics center. The server is primarily responsible for data aggregation and analysis, while the terminals are responsible for collecting location information. Details of the embodiment are shown below.

[0328] The device periodically collects location information as the user or device moves. This location information is obtained using a GPS module or Wi-Fi, encrypted within the device, and sent to the server. The server receives the location information using a Python program or HTTP request library and records it in a database as real-time time-series data.

[0329] The server analyzes the received location information and removes duplicate and incomplete data. Advanced data processing techniques are used to ensure clean data in order to generate a standardized dataset. Furthermore, a generative AI model is used to predict patterns for optimizing movement routes within the logistics center, supporting route suggestions and efficiency improvements.

[0330] As a concrete example, consider an operational scenario in a large logistics center. By utilizing this system when logistics robots move goods at various locations, it becomes possible to avoid congested areas and set the most efficient routes. This improves delivery speed and increases the volume of goods handled per hour.

[0331] Example of a prompt:

[0332] "Please explain the method for generating models using location data to optimize robot movement within a logistics center."

[0333] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0334] Step 1:

[0335] The device acquires location information. When user movement is detected, the device uses its GPS module and Wi-Fi to obtain its current location. This information includes location coordinates and a timestamp. The acquired data is encrypted and prepared to be sent to the server.

[0336] Step 2:

[0337] The server receives location information. The server receives encrypted location data sent from the terminal and stores it in its database. At this time, the received data is recorded in real time and enters a waiting state for subsequent data processing.

[0338] Step 3:

[0339] The server performs data cleansing. It analyzes the received location data and removes duplicate and incomplete data. A data duplication checking algorithm is used to create a clean and consistent dataset. The output is an optimized standard dataset.

[0340] Step 4:

[0341] The server applies a generated AI model. The generated AI model, using a clean dataset as input, generates a model that predicts efficient robot movement within the logistics center. This allows for real-time proposal of the most likely optimal route, thereby improving movement efficiency.

[0342] Step 5:

[0343] The user receives optimized data and suggestions. The server presents the user with predicted optimal route information, which is then used in actual operations within the logistics center. Based on the information provided, the user adjusts the movement of robots and personnel to achieve efficient logistics management.

[0344] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0345] This invention is a system that recognizes user emotions and combines that data with location information to provide a richer dataset. This system enables the collection, integration, analysis, and provision of various types of data, and facilitates the development and utilization of generative AI models.

[0346] System Configuration

[0347] 1. Collection of emotional data and location information by devices

[0348] Users install an application that enables emotion recognition using their smartphones or wearable devices. The device analyzes the subject's voice, facial expressions, and biosignals, and acquires emotion data using an emotion engine. Simultaneously, it collects location information, packets both types of data, and sends them to the server.

[0349] 2. Data integration and processing by the server

[0350] The server stores sentiment data and location information received from multiple terminals in a time-series database. During integration, it detects data duplication and missing data and processes it to ensure it is stored in a standardized format. It also evaluates the relationship between sentiment data and location information and constructs the necessary datasets.

[0351] 3. Sales and provision of data on the market

[0352] Users (data buyers) can search for datasets related to specific emotional states or geographical locations through the online platform. Customized datasets, tailored to the buyer's requirements, are provided in an appropriate format and delivered via secure payment.

[0353] 4. Profit sharing

[0354] The server distributes revenue to data providers who provided emotional data and location information, based on the sales generated. Revenue is calculated and distributed fairly based on the utilization value of the data.

[0355] Specific example

[0356] Acquisition and utilization of emotional data

[0357] The device recognizes the user's emotions, such as joy or sadness, and also acquires their location information at that time. This data is used by marketing companies to analyze the effectiveness of advertisements.

[0358] Providing custom datasets

[0359] In a server-based service, if a retailer wants to know the sentiment trends of consumers in a specific area, the server generates and provides a dataset based on those conditions.

[0360] This invention maximizes the impact of location information with added emotional data on the generated AI model, enabling the provision of new business insights. Through this system, more advanced and context-sensitive data utilization is expected.

[0361] The following describes the processing flow.

[0362] Step 1:

[0363] The device uses an emotion recognition application installed on the user's device to acquire emotional data from the user's voice, facial expressions, and biosignals. The emotion engine analyzes this data in real time to identify the emotional state.

[0364] Step 2:

[0365] The device acquires emotional data and simultaneously collects current location information using its GPS function. The emotional data and location information are paired and encrypted as a single data packet.

[0366] Step 3:

[0367] The device sends encrypted data packets to the server via the internet connection specified by the user. This transmission uses protocols (e.g., HTTPS) that protect data accuracy and privacy.

[0368] Step 4:

[0369] The server analyzes the received data packets and stores sentiment data and location information in a time-series database. The data integration process removes duplicate data and detects and corrects missing data.

[0370] Step 5:

[0371] The server generates custom datasets linked to sentiment and location based on requests from buyers who use the data. This process involves filtering and aggregation to address specific marketing needs and analytical requests.

[0372] Step 6:

[0373] Users (data buyers) access the data through an online platform, select and purchase the custom datasets they need. The server conducts the transaction through a secure payment system and delivers the data to the buyer.

[0374] Step 7:

[0375] The server distributes revenue to data providers. It calculates the distribution based on the transaction amount and automatically transfers it to the data provider's registered account. This process is conducted fairly and transparently.

[0376] (Example 2)

[0377] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0378] In modern society, the importance of effectively integrating people's emotions and location information for commercial and research purposes is increasing. However, challenges remain, including inefficiencies from processing emotional and geographical data separately, the problem of duplicate data, and the difficulty of fairly distributing revenue to data providers. This makes it difficult to accurately utilize data and maintain the motivation of data providers.

[0379] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0380] In this invention, the server includes means for acquiring emotional data and location data from users, means for integrating the acquired emotional data and location data to remove duplicate and incomplete data, and means for making the integrated data available for development and utilization of generating AI models. This enables the effective integration of emotional information and location information to enhance commercial value while simultaneously allowing for fair revenue sharing to data providers.

[0381] A "user" refers to a person who provides emotional data and location data using this system.

[0382] "Emotional data" refers to information about a user's emotions obtained by analyzing their voice, facial expressions, and biosignals.

[0383] "Location data" refers to data that indicates a user's geographical location, and primarily refers to information obtained using sensor technologies such as GPS.

[0384] A "server" refers to a centralized computer system that receives, integrates, stores, analyzes, and provides data sent from terminals.

[0385] A "generative AI model" refers to an intelligent model developed using machine learning techniques to assist human tasks.

[0386] "Integration method" refers to the process by which the server combines the emotional and location data it receives, eliminating duplication and omissions, and converts it into a format that is easy to analyze.

[0387] A "revenue sharing mechanism" refers to a calculation and payment system that allows a server to fairly distribute revenue to data providers based on the amount of data traded.

[0388] A "dataset" refers to a collection of data organized based on specific criteria and used for analysis or trading.

[0389] This invention is a system that collects and integrates user emotion data and location data to provide it as a marketable dataset. Users install an emotion recognition application on a device such as a smartphone or wearable device. This application is designed using AI technology and is capable of voice recognition, facial recognition, and biosignal analysis. The device collects emotion data by analyzing the user's voice, facial expressions, and biosignals. Simultaneously, it acquires accurate location data using a GPS sensor.

[0390] The emotional and location data collected by the device are combined into a single packet and sent to the server using a secure communication protocol. The server stores the received data in a time-series database and performs integration and analysis. While checking for data duplication and missing information, it utilizes a generative AI model to evaluate the relationship between emotional and location information and generates a dataset that can be used for commercial and research purposes.

[0391] One concrete example of using this system is for marketing purposes. For instance, if a company is running an advertising campaign for a new product and wants to understand the emotional response of consumers in a specific region, the server can provide a dataset that matches those criteria.

[0392] Possible prompts for the generative AI model include: "Generate a dataset of users who are happy in a specific region and analyze how to maximize the effectiveness of advertising campaigns."

[0393] This system makes it possible to gain new business insights by effectively utilizing location data with added emotional information. A mechanism for fair and effective revenue sharing with data providers is also incorporated.

[0394] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0395] Step 1:

[0396] The user installs an emotion recognition app on their smartphone or wearable device. When the app is launched, it receives the user's voice, facial expressions, and biosignals as input data. The device analyzes this data using an AI model and outputs emotion data that identifies the user's emotional state.

[0397] Step 2:

[0398] The device simultaneously uses a GPS module to obtain precise location data. This location data indicates the user's current location and is packetized together with sentiment data. This packet contains information about the user's location and the emotions they are experiencing.

[0399] Step 3:

[0400] The device transmits acquired emotion and location data to the server via a secure communication protocol. The input here is data packets from the device, which the server receives and stores in time-series data storage.

[0401] Step 4:

[0402] The server analyzes the received data packets and performs a process to automatically clean up duplicate and incomplete data. The data processing performed by the server includes filtering to improve data accuracy. The output is an integrated, cleaned dataset.

[0403] Step 5:

[0404] The server uses a generative AI model to evaluate the relationship between sentiment data and location data. This processing step involves analyzing a cleaned dataset as input and generating a commercially usable dataset as output.

[0405] Step 6:

[0406] The server provides customized datasets to data buyers. When a user requests a specific dataset through the online platform, the server searches for data that matches the criteria and outputs it in the appropriate format.

[0407] Step 7:

[0408] The server distributes the revenue generated from the sale of data to the data providers. In this step, transaction revenue is used as input, and a fair revenue distribution is performed based on a pre-configured algorithm. The output is the fairly calculated reward.

[0409] (Application Example 2)

[0410] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0411] Traditional advertising delivery systems have the challenge of not adequately providing personalized advertising experiences that respond to users' emotions and circumstances. Furthermore, it has been difficult to efficiently integrate emotional data and location information and provide it as marketable data.

[0412] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0413] In this invention, the server includes means for acquiring emotional data from users, means for acquiring location information from data provision terminals, means for integrating the acquired emotional data and location information to remove duplicate and incomplete data, means for generating marketable information based on the integrated emotional data and location information, and means for presenting personalized data according to the conditions of the information purchaser. This makes it possible to provide an advertising experience that is tailored to the user's emotions and circumstances.

[0414] "Emotional data" refers to data acquired to represent a user's emotional state, and is obtained by analyzing voice, facial expressions, and biosignals.

[0415] "Location information" refers to geographical data used to determine the current location of a device, and is obtained using methods such as GPS.

[0416] "Integration" refers to the process of combining information obtained from multiple data sources into a single, standardized format, removing redundancies and missing data.

[0417] "Personalized data" refers to datasets that have been customized according to the specific conditions and requirements of the information purchaser.

[0418] "Marketable information" refers to information that integrates acquired sentiment data and location information in a commercially valuable form, making it available for buying and selling on the data market.

[0419] "Providing an advertising experience" means presenting the most relevant advertisements to users based on their emotional state and location information.

[0420] To implement this invention, a system is needed to collect user emotion data and integrate it with location information. Specifically, terminals such as smartphones and wearable devices are used, and emotion recognition applications are installed on these terminals. The emotion recognition application analyzes voice, facial expressions, and biosignals to generate user emotion data. Location information is obtained using GPS.

[0421] The device sends acquired emotion data and location information to the server. The server integrates this data using the Google Maps API and time-series databases. It checks for duplicate or missing data, and stores it in a standardized format. A generative AI model is used to select the most relevant advertisements for the user's situation from the integrated data.

[0422] The server delivers personalized advertisements to users in real time through the ad delivery network. For example, if a user expresses joy while in a specific commercial area, it can instantly deliver advertisements to that user that include discount information for nearby stores.

[0423] As a concrete example, the prompt "Select the most suitable advertisement when the user expresses surprise in a specific area. For example, consider a special invitation advertisement for a nearby new attraction." is used to perform data analysis and ad selection by a generative AI model.

[0424] This makes it possible to provide personalized advertising experiences that utilize emotional data and location information, enabling the delivery of information tailored to the user's situation.

[0425] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0426] Step 1:

[0427] The device analyzes the user's voice, facial expressions, and biosignals using an emotion recognition application to acquire emotional data. Inputs include voice, facial images, and biosignals, which are processed by an emotion analysis algorithm to output quantitative data representing the user's emotions, such as joy or surprise.

[0428] Step 2:

[0429] The device acquires location information from its GPS sensor along with the acquired emotional data. It receives emotional data and geographical coordinates as input, packets them into an integrated dataset, and sends it to the server. This integrated data links the user's emotional state with their location at that time.

[0430] Step 3:

[0431] The server uses the Google Maps API to match received sentiment data and location information with map data and stores it in a time-series database. The input consists of sentiment data and location information, which are converted into corresponding geographical information, checked for duplicates and missing data, and then output in a standardized format.

[0432] Step 4:

[0433] The server uses a generative AI model to select the most suitable advertisement based on the user's current situation from integrated data. Input consists of user data and prompt messages stored in a time-series database, and individual advertisement selection is performed using a machine learning algorithm. The output is the selected advertisement information.

[0434] Step 5:

[0435] The server sends selected advertisements to the device and displays them to the user. It utilizes an ad delivery network to provide real-time advertising. The input is advertising information, and the output is the display of advertisements on the user's device screen.

[0436] Step 6:

[0437] When a user responds to an advertisement (by clicking or taking action), the server collects this data and uses it to analyze the effectiveness of the advertisement and improve future selection algorithms. The input is user action data, and the output is the results of the advertisement effectiveness analysis.

[0438] 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.

[0439] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0440] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart glasses 214.

[0441] [Third Embodiment]

[0442] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0443] 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.

[0444] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0445] 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.

[0446] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0447] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0448] 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.

[0449] 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.

[0450] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0451] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0452] In the headset terminal 314, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0453] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the headset terminal 314 will be referred to as the "terminal".

[0454] This invention is a system for effectively collecting real-world data and facilitating the development of generative AI models. Specific embodiments for carrying out this invention are described in detail below.

[0455] System Configuration

[0456] 1. Data collection by devices

[0457] Users grant permission to share their location information on their smartphones and other mobile devices. The device periodically collects location information with the user's permission, using GPS and Wi-Fi data. The collected data is encrypted and sent to the server.

[0458] 2. Data integration and processing by the server

[0459] The server receives and analyzes location information transmitted from multiple terminals. The data is stored in a time-series database, and duplicates and incomplete data are removed. Data integration creates a clean dataset in a standardized format.

[0460] 3. Buying, selling, and providing data on the marketplace.

[0461] Users (data buyers) can access the platform online and search for datasets that meet their needs. The data is provided in custom formats filtered by different criteria. The datasets selected by buyers are traded through secure online payments.

[0462] 4. Profit sharing

[0463] The server distributes revenue to data providers based on the sales generated. The distribution is calculated based on the volume and frequency of data use and transferred to the provider's registered financial institution account. The revenue structure is designed to ensure fair distribution to each data provider.

[0464] Specific example

[0465] Location information collection and transmission

[0466] When a device visits a store, its location information is transmitted to the server in real time. The frequency of data collection by the device can be adjusted in the application settings.

[0467] Customizing and selling datasets

[0468] In a data marketplace provided by a server, if a retailer requests customer visit patterns for a specific metropolitan area, a dataset tailored to those conditions is generated and provided.

[0469] This invention enables the efficient use of location information and other real-world data, allowing for the collection of large amounts of data that form the basis for AI model development. Through this system, a platform is realized that benefits both data providers and buyers.

[0470] The following describes the processing flow.

[0471] Step 1:

[0472] The device collects location data from devices that have been authorized to share their location. Location information is acquired periodically and its accuracy is maintained based on GPS signals and Wi-Fi networks.

[0473] Step 2:

[0474] The device converts the collected location information into data packets, encrypts them to ensure security, and then transmits them to the server via the internet.

[0475] Step 3:

[0476] The server analyzes the received location data and stores it in a time-series database. The data is formatted to a standardized format, and duplicate or incomplete data is removed using algorithms.

[0477] Step 4:

[0478] The server prepares the integrated dataset for listing on the data marketplace. The data is tagged so that it can be filtered by various metrics.

[0479] Step 5:

[0480] Users (data buyers) access the data marketplace and search for datasets that meet specific criteria. The selected data is then customized to the buyer's needs.

[0481] Step 6:

[0482] The server provides the data set selected by the buyer through a secure payment system. Once payment is complete, the data is transferred to the buyer.

[0483] Step 7:

[0484] The server calculates revenue for data providers based on data transaction information. It automatically transfers the revenue-sharing payments to registered bank accounts.

[0485] (Example 1)

[0486] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0487] Conventional location data collection systems have been inefficient in collecting data from users, and processes for encryption and ensuring data reliability have been inadequate. Furthermore, in the data marketplace, the processes of providing and purchasing data have not adequately generated customized datasets that meet user needs or ensured fair revenue sharing. There is a need to solve these problems and provide a system that efficiently and securely collects, processes, and trades location data.

[0488] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0489] In this invention, the server includes means for encrypting location information and transmitting it to the server using a secure communication protocol, means for storing the decrypted location information in a time-series data store, and means for analyzing the integrated location information, removing duplicate and incomplete data, and generating a clean dataset in a standardized format. This enables efficient and secure collection of location information from users, highly reliable data processing, and rapid data provision to the market.

[0490] A "data provision terminal" is an electronic device used to acquire location information from a user and transmit it to a server.

[0491] "Location information" refers to information about the user's current location obtained by the data provision terminal.

[0492] "Encryption" is the process of transforming data into a format that cannot be read by third parties in order to protect the confidentiality of the data.

[0493] A "secure communication protocol" is a network communication method designed to protect the confidentiality and integrity of data during transmission.

[0494] A "server" is a central computing device that receives data sent from terminals, processes it, and stores and manages it.

[0495] "Decryption" is the process of returning encrypted data to its original, readable format.

[0496] A "time-series data store" is a data management system for organizing and storing data based on time.

[0497] "Duplicate data" refers to information within a dataset where the same content is recorded multiple times.

[0498] "Incomplete data" refers to data that lacks necessary information, making it impossible to perform sufficient judgments or processing.

[0499] A "standardized format" is a set of criteria for organizing data recorded in different formats or units into a unified form.

[0500] A "clean dataset" is a collection of consistent and accurate data from which duplicates and errors have been removed.

[0501] A "data marketplace" is an online platform where data providers sell their data, and data buyers purchase and use that data.

[0502] A "custom dataset" is a collection of data that has been processed based on specific conditions or needs.

[0503] "Revenue sharing" is the process of fairly distributing the profits earned from data trading to data providers.

[0504] This invention provides a system that enables the efficient and secure collection, processing, and trading of location data. This system includes the following main components:

[0505] 1. Collection of location information by the device

[0506] Users grant permission to share their location information on their smartphones and other mobile devices. With the user's consent, the device periodically acquires location information using GPS and Wi-Fi signals. This allows for real-time tracking of the user's movements and current location. Furthermore, the location information is immediately encrypted using the AES encryption algorithm, ensuring the security of the information.

[0507] 2. Encryption and Data Transmission

[0508] The device sends encrypted location information to the server using a secure communication protocol (e.g., HTTPS). This process reduces the risk of eavesdropping or tampering during data transmission.

[0509] 3. Receiving and processing data by the server

[0510] The server decrypts the encrypted data received from the terminal and stores it in a time-series data store. This data store maintains data integrity and appropriately removes duplicate and incomplete data. As a result, a standardized, clean dataset is generated.

[0511] 4. Trading in the data market

[0512] Users (data buyers) can search for datasets tailored to their needs through an online platform. The server generates custom datasets in response to the data buyer's request and provides the necessary data. Buyers can purchase datasets through a secure online payment system and access them immediately. A specific example of a prompt might be, "I want to analyze the weekly visit patterns of visitors in a specific region. Please provide a suitable dataset."

[0513] This system creates an innovative platform that benefits both data providers and buyers, effectively providing the data infrastructure necessary for developing generative AI models.

[0514] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0515] Step 1:

[0516] The device obtains permission from the user to share its location. Upon receiving this input, the device acquires the user's location information using GPS or Wi-Fi. This location information is displayed on the electronic device in the form of latitude and longitude. The device then encrypts the acquired location information using the AES encryption algorithm. This encrypted data becomes the output.

[0517] Step 2:

[0518] The device sends encrypted location information to the server using a secure communication protocol (such as HTTPS). Using this protocol reduces the risk of third-party interception of the data during transmission. The output confirms that the encrypted data has safely reached the server.

[0519] Step 3:

[0520] The server receives encrypted data from the terminal as input. The server uses the decryption key to decrypt the data and restore it to its original location information. This decrypted location information is stored in a time-series data store. Here, duplicate and missing data are checked, and a clean dataset is output.

[0521] Step 4:

[0522] The server processes a clean dataset into a format suitable for the data market. Based on the request criteria from data buyers, it generates custom datasets filtered according to specific conditions. During this process, the dataset is evaluated to ensure it accurately matches the buyer's requirements, and a customized dataset is generated as output.

[0523] Step 5:

[0524] Users (data buyers) search for and purchase custom datasets on an online platform. Payment is made using a secure online payment system based on the user's selected dataset. An accessible download link is generated immediately after the purchase is complete.

[0525] Step 6:

[0526] The server calculates the transaction value obtained from the sale of datasets. Based on this transaction value, it distributes revenue to data providers. Revenue is calculated considering the amount of data provided and the frequency of use, and is transferred to the data provider's registered financial institution account. As an output, a fair revenue distribution is implemented.

[0527] (Application Example 1)

[0528] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0529] In large facilities such as logistics centers, there is a need to optimize the movement of mobile equipment such as robots and to manage them efficiently. However, currently, highly accurate optimization based on sufficient information is not being performed, which can lead to logistics congestion and unexpected delays. A system is needed to resolve this problem.

[0530] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0531] In this invention, the server includes a device for acquiring location information, a device for integrating the acquired location information and removing duplicate or incomplete data, and a device for optimizing logistics management using a generative AI model. This enables efficient optimization of the movement of mobile equipment in a logistics center and highly accurate logistics management.

[0532] A "location information acquisition device" is a device that has the function of detecting the user's location using GPS or Wi-Fi data in order to identify the user's travel route and place of stay.

[0533] A "device that integrates acquired location information and removes duplicate and incomplete data" is a device that aggregates location information obtained from multiple data sources and has the function of eliminating duplicate information and incomplete data in order to generate a consistent and clean dataset.

[0534] A "device for optimizing logistics management using generative AI models" is a system that generates algorithms and predictive models to optimize the movement and work efficiency of equipment and robots within a logistics center, based on aggregated location information.

[0535] A "device for distributing revenue to data providers according to transaction amount" is a system that has the function of distributing revenue obtained from selling acquired data in proportion to the transaction amount, in order to fairly return that revenue to data providers.

[0536] This invention is a system that links a server and terminals to optimize the movement of mobile equipment in a logistics center. The server is primarily responsible for data aggregation and analysis, while the terminals are responsible for collecting location information. Details of the embodiment are shown below.

[0537] The device periodically collects location information as the user or device moves. This location information is obtained using a GPS module or Wi-Fi, encrypted within the device, and sent to the server. The server receives the location information using a Python program or HTTP request library and records it in a database as real-time time-series data.

[0538] The server analyzes the received location information and removes duplicate and incomplete data. Advanced data processing techniques are used to ensure clean data in order to generate a standardized dataset. Furthermore, a generative AI model is used to predict patterns for optimizing movement routes within the logistics center, supporting route suggestions and efficiency improvements.

[0539] As a concrete example, consider an operational scenario in a large logistics center. By utilizing this system when logistics robots move goods at various locations, it becomes possible to avoid congested areas and set the most efficient routes. This improves delivery speed and increases the volume of goods handled per hour.

[0540] Example of a prompt:

[0541] "Please explain the method for generating models using location data to optimize robot movement within a logistics center."

[0542] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0543] Step 1:

[0544] The device acquires location information. When user movement is detected, the device uses its GPS module and Wi-Fi to obtain its current location. This information includes location coordinates and a timestamp. The acquired data is encrypted and prepared to be sent to the server.

[0545] Step 2:

[0546] The server receives location information. The server receives encrypted location data sent from the terminal and stores it in its database. At this time, the received data is recorded in real time and enters a waiting state for subsequent data processing.

[0547] Step 3:

[0548] The server performs data cleansing. It analyzes the received location data and removes duplicate and incomplete data. A data duplication checking algorithm is used to create a clean and consistent dataset. The output is an optimized standard dataset.

[0549] Step 4:

[0550] The server applies a generated AI model. The generated AI model, using a clean dataset as input, generates a model that predicts efficient robot movement within the logistics center. This allows for real-time proposal of the most likely optimal route, thereby improving movement efficiency.

[0551] Step 5:

[0552] The user receives optimized data and suggestions. The server presents the user with predicted optimal route information, which is then used in actual operations within the logistics center. Based on the information provided, the user adjusts the movement of robots and personnel to achieve efficient logistics management.

[0553] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0554] This invention is a system that recognizes user emotions and combines that data with location information to provide a richer dataset. This system enables the collection, integration, analysis, and provision of various types of data, and facilitates the development and utilization of generative AI models.

[0555] System Configuration

[0556] 1. Collection of emotional data and location information by devices

[0557] Users install an application that enables emotion recognition using their smartphones or wearable devices. The device analyzes the subject's voice, facial expressions, and biosignals, and acquires emotion data using an emotion engine. Simultaneously, it collects location information, packets both types of data, and sends them to the server.

[0558] 2. Data integration and processing by the server

[0559] The server stores sentiment data and location information received from multiple terminals in a time-series database. During integration, it detects data duplication and missing data and processes it to ensure it is stored in a standardized format. It also evaluates the relationship between sentiment data and location information and constructs the necessary datasets.

[0560] 3. Sales and provision of data on the market

[0561] Users (data buyers) can search for datasets related to specific emotional states or geographical locations through the online platform. Customized datasets, tailored to the buyer's requirements, are provided in an appropriate format and delivered via secure payment.

[0562] 4. Profit sharing

[0563] The server distributes revenue to data providers who provided emotional data and location information, based on the sales generated. Revenue is calculated and distributed fairly based on the utilization value of the data.

[0564] Specific example

[0565] Acquisition and utilization of emotional data

[0566] The device recognizes the user's emotions, such as joy or sadness, and also acquires their location information at that time. This data is used by marketing companies to analyze the effectiveness of advertisements.

[0567] Providing custom datasets

[0568] In a server-based service, if a retailer wants to know the sentiment trends of consumers in a specific area, the server generates and provides a dataset based on those conditions.

[0569] This invention maximizes the impact of location information with added emotional data on the generated AI model, enabling the provision of new business insights. Through this system, more advanced and context-sensitive data utilization is expected.

[0570] The following describes the processing flow.

[0571] Step 1:

[0572] The device uses an emotion recognition application installed on the user's device to acquire emotional data from the user's voice, facial expressions, and biosignals. The emotion engine analyzes this data in real time to identify the emotional state.

[0573] Step 2:

[0574] The device acquires emotional data and simultaneously collects current location information using its GPS function. The emotional data and location information are paired and encrypted as a single data packet.

[0575] Step 3:

[0576] The device sends encrypted data packets to the server via the internet connection specified by the user. This transmission uses protocols (e.g., HTTPS) that protect data accuracy and privacy.

[0577] Step 4:

[0578] The server analyzes the received data packets and stores sentiment data and location information in a time-series database. The data integration process removes duplicate data and detects and corrects missing data.

[0579] Step 5:

[0580] The server generates custom datasets linked to sentiment and location based on requests from buyers who use the data. This process involves filtering and aggregation to address specific marketing needs and analytical requests.

[0581] Step 6:

[0582] Users (data buyers) access the data through an online platform, select and purchase the custom datasets they need. The server conducts the transaction through a secure payment system and delivers the data to the buyer.

[0583] Step 7:

[0584] The server distributes revenue to data providers. It calculates the distribution based on the transaction amount and automatically transfers it to the data provider's registered account. This process is conducted fairly and transparently.

[0585] (Example 2)

[0586] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0587] In modern society, the importance of effectively integrating people's emotions and location information for commercial and research purposes is increasing. However, challenges remain, including inefficiencies from processing emotional and geographical data separately, the problem of duplicate data, and the difficulty of fairly distributing revenue to data providers. This makes it difficult to accurately utilize data and maintain the motivation of data providers.

[0588] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0589] In this invention, the server includes means for acquiring emotional data and location data from users, means for integrating the acquired emotional data and location data to remove duplicate and incomplete data, and means for making the integrated data available for development and utilization of generating AI models. This enables the effective integration of emotional information and location information to enhance commercial value while simultaneously allowing for fair revenue sharing to data providers.

[0590] A "user" refers to a person who provides emotional data and location data using this system.

[0591] "Emotional data" refers to information about a user's emotions obtained by analyzing their voice, facial expressions, and biosignals.

[0592] "Location data" refers to data that indicates a user's geographical location, and primarily refers to information obtained using sensor technologies such as GPS.

[0593] A "server" refers to a centralized computer system that receives, integrates, stores, analyzes, and provides data sent from terminals.

[0594] A "generative AI model" refers to an intelligent model developed using machine learning techniques to assist human tasks.

[0595] "Integration method" refers to the process by which the server combines the emotional and location data it receives, eliminating duplication and omissions, and converts it into a format that is easy to analyze.

[0596] A "revenue sharing mechanism" refers to a calculation and payment system that allows a server to fairly distribute revenue to data providers based on the amount of data traded.

[0597] A "dataset" refers to a collection of data organized based on specific criteria and used for analysis or trading.

[0598] This invention is a system that collects and integrates user emotion data and location data to provide it as a marketable dataset. Users install an emotion recognition application on a device such as a smartphone or wearable device. This application is designed using AI technology and is capable of voice recognition, facial recognition, and biosignal analysis. The device collects emotion data by analyzing the user's voice, facial expressions, and biosignals. Simultaneously, it acquires accurate location data using a GPS sensor.

[0599] The emotional and location data collected by the device are combined into a single packet and sent to the server using a secure communication protocol. The server stores the received data in a time-series database and performs integration and analysis. While checking for data duplication and missing information, it utilizes a generative AI model to evaluate the relationship between emotional and location information and generates a dataset that can be used for commercial and research purposes.

[0600] One concrete example of using this system is for marketing purposes. For instance, if a company is running an advertising campaign for a new product and wants to understand the emotional response of consumers in a specific region, the server can provide a dataset that matches those criteria.

[0601] Possible prompts for the generative AI model include: "Generate a dataset of users who are happy in a specific region and analyze how to maximize the effectiveness of advertising campaigns."

[0602] This system makes it possible to gain new business insights by effectively utilizing location data with added emotional information. A mechanism for fair and effective revenue sharing with data providers is also incorporated.

[0603] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0604] Step 1:

[0605] The user installs an emotion recognition app on their smartphone or wearable device. When the app is launched, it receives the user's voice, facial expressions, and biosignals as input data. The device analyzes this data using an AI model and outputs emotion data that identifies the user's emotional state.

[0606] Step 2:

[0607] The device simultaneously uses a GPS module to obtain precise location data. This location data indicates the user's current location and is packetized together with sentiment data. This packet contains information about the user's location and the emotions they are experiencing.

[0608] Step 3:

[0609] The device transmits acquired emotion and location data to the server via a secure communication protocol. The input here is data packets from the device, which the server receives and stores in time-series data storage.

[0610] Step 4:

[0611] The server analyzes the received data packets and performs a process to automatically clean up duplicate and incomplete data. The data processing performed by the server includes filtering to improve data accuracy. The output is an integrated, cleaned dataset.

[0612] Step 5:

[0613] The server uses a generative AI model to evaluate the relationship between sentiment data and location data. This processing step involves analyzing a cleaned dataset as input and generating a commercially usable dataset as output.

[0614] Step 6:

[0615] The server provides customized datasets to data buyers. When a user requests a specific dataset through the online platform, the server searches for data that matches the criteria and outputs it in the appropriate format.

[0616] Step 7:

[0617] The server distributes the revenue generated from the sale of data to the data providers. In this step, transaction revenue is used as input, and a fair revenue distribution is performed based on a pre-configured algorithm. The output is the fairly calculated reward.

[0618] (Application Example 2)

[0619] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0620] Traditional advertising delivery systems have the challenge of not adequately providing personalized advertising experiences that respond to users' emotions and circumstances. Furthermore, it has been difficult to efficiently integrate emotional data and location information and provide it as marketable data.

[0621] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0622] In this invention, the server includes means for acquiring emotional data from users, means for acquiring location information from data provision terminals, means for integrating the acquired emotional data and location information to remove duplicate and incomplete data, means for generating marketable information based on the integrated emotional data and location information, and means for presenting personalized data according to the conditions of the information purchaser. This makes it possible to provide an advertising experience that is tailored to the user's emotions and circumstances.

[0623] "Emotional data" refers to data acquired to represent a user's emotional state, and is obtained by analyzing voice, facial expressions, and biosignals.

[0624] "Location information" refers to geographical data used to determine the current location of a device, and is obtained using methods such as GPS.

[0625] "Integration" refers to the process of combining information obtained from multiple data sources into a single, standardized format, removing redundancies and missing data.

[0626] "Personalized data" refers to datasets that have been customized according to the specific conditions and requirements of the information purchaser.

[0627] "Marketable information" refers to information that integrates acquired sentiment data and location information in a commercially valuable form, making it available for buying and selling on the data market.

[0628] "Providing an advertising experience" means presenting the most relevant advertisements to users based on their emotional state and location information.

[0629] To implement this invention, a system is needed to collect user emotion data and integrate it with location information. Specifically, terminals such as smartphones and wearable devices are used, and emotion recognition applications are installed on these terminals. The emotion recognition application analyzes voice, facial expressions, and biosignals to generate user emotion data. Location information is obtained using GPS.

[0630] The device sends acquired emotion data and location information to the server. The server integrates this data using the Google Maps API and time-series databases. It checks for duplicate or missing data, and stores it in a standardized format. A generative AI model is used to select the most relevant advertisements for the user's situation from the integrated data.

[0631] The server delivers personalized advertisements to users in real time through the ad delivery network. For example, if a user expresses joy while in a specific commercial area, it can instantly deliver advertisements to that user that include discount information for nearby stores.

[0632] As a concrete example, the prompt "Select the most suitable advertisement when the user expresses surprise in a specific area. For example, consider a special invitation advertisement for a nearby new attraction." is used to perform data analysis and ad selection by a generative AI model.

[0633] This makes it possible to provide personalized advertising experiences that utilize emotional data and location information, enabling the delivery of information tailored to the user's situation.

[0634] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0635] Step 1:

[0636] The device analyzes the user's voice, facial expressions, and biosignals using an emotion recognition application to acquire emotional data. Inputs include voice, facial images, and biosignals, which are processed by an emotion analysis algorithm to output quantitative data representing the user's emotions, such as joy or surprise.

[0637] Step 2:

[0638] The device acquires location information from its GPS sensor along with the acquired emotional data. It receives emotional data and geographical coordinates as input, packets them into an integrated dataset, and sends it to the server. This integrated data links the user's emotional state with their location at that time.

[0639] Step 3:

[0640] The server uses the Google Maps API to match received sentiment data and location information with map data and stores it in a time-series database. The input consists of sentiment data and location information, which are converted into corresponding geographical information, checked for duplicates and missing data, and then output in a standardized format.

[0641] Step 4:

[0642] The server uses a generative AI model to select the most suitable advertisement based on the user's current situation from integrated data. Input consists of user data and prompt messages stored in a time-series database, and individual advertisement selection is performed using a machine learning algorithm. The output is the selected advertisement information.

[0643] Step 5:

[0644] The server sends selected advertisements to the device and displays them to the user. It utilizes an ad delivery network to provide real-time advertising. The input is advertising information, and the output is the display of advertisements on the user's device screen.

[0645] Step 6:

[0646] When a user responds to an advertisement (by clicking or taking action), the server collects this data and uses it to analyze the effectiveness of the advertisement and improve future selection algorithms. The input is user action data, and the output is the results of the advertisement effectiveness analysis.

[0647] 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.

[0648] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0649] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and specific processing may also be performed by the headset terminal 314.

[0650] [Fourth Embodiment]

[0651] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0652] 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.

[0653] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0654] 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.

[0655] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0656] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0657] 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.

[0658] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. Furthermore, the robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0659] 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.

[0660] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0661] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0662] In robot 414, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0663] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0664] This invention is a system for effectively collecting real-world data and facilitating the development of generative AI models. Specific embodiments for carrying out this invention are described in detail below.

[0665] System Configuration

[0666] 1. Data collection by devices

[0667] Users grant permission to share their location information on their smartphones and other mobile devices. The device periodically collects location information with the user's permission, using GPS and Wi-Fi data. The collected data is encrypted and sent to the server.

[0668] 2. Data integration and processing by the server

[0669] The server receives and analyzes location information transmitted from multiple terminals. The data is stored in a time-series database, and duplicates and incomplete data are removed. Data integration creates a clean dataset in a standardized format.

[0670] 3. Buying, selling, and providing data on the marketplace.

[0671] Users (data buyers) can access the platform online and search for datasets that meet their needs. The data is provided in custom formats filtered by different criteria. The datasets selected by buyers are traded through secure online payments.

[0672] 4. Profit sharing

[0673] The server distributes revenue to data providers based on the sales generated. The distribution is calculated based on the volume and frequency of data use and transferred to the provider's registered financial institution account. The revenue structure is designed to ensure fair distribution to each data provider.

[0674] Specific example

[0675] Location information collection and transmission

[0676] When a device visits a store, its location information is transmitted to the server in real time. The frequency of data collection by the device can be adjusted in the application settings.

[0677] Customizing and selling datasets

[0678] In a data marketplace provided by a server, if a retailer requests customer visit patterns for a specific metropolitan area, a dataset tailored to those conditions is generated and provided.

[0679] This invention enables the efficient use of location information and other real-world data, allowing for the collection of large amounts of data that form the basis for AI model development. Through this system, a platform is realized that benefits both data providers and buyers.

[0680] The following describes the processing flow.

[0681] Step 1:

[0682] The device collects location data from devices that have been authorized to share their location. Location information is acquired periodically and its accuracy is maintained based on GPS signals and Wi-Fi networks.

[0683] Step 2:

[0684] The device converts the collected location information into data packets, encrypts them to ensure security, and then transmits them to the server via the internet.

[0685] Step 3:

[0686] The server analyzes the received location data and stores it in a time-series database. The data is formatted to a standardized format, and duplicate or incomplete data is removed using algorithms.

[0687] Step 4:

[0688] The server prepares the integrated dataset for listing on the data marketplace. The data is tagged so that it can be filtered by various metrics.

[0689] Step 5:

[0690] Users (data buyers) access the data marketplace and search for datasets that meet specific criteria. The selected data is then customized to the buyer's needs.

[0691] Step 6:

[0692] The server provides the data set selected by the buyer through a secure payment system. Once payment is complete, the data is transferred to the buyer.

[0693] Step 7:

[0694] The server calculates revenue for data providers based on data transaction information. It automatically transfers the revenue-sharing payments to registered bank accounts.

[0695] (Example 1)

[0696] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0697] Conventional location data collection systems have been inefficient in collecting data from users, and processes for encryption and ensuring data reliability have been inadequate. Furthermore, in the data marketplace, the processes of providing and purchasing data have not adequately generated customized datasets that meet user needs or ensured fair revenue sharing. There is a need to solve these problems and provide a system that efficiently and securely collects, processes, and trades location data.

[0698] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0699] In this invention, the server includes means for encrypting location information and transmitting it to the server using a secure communication protocol, means for storing the decrypted location information in a time-series data store, and means for analyzing the integrated location information, removing duplicate and incomplete data, and generating a clean dataset in a standardized format. This enables efficient and secure collection of location information from users, highly reliable data processing, and rapid data provision to the market.

[0700] A "data provision terminal" is an electronic device used to acquire location information from a user and transmit it to a server.

[0701] "Location information" refers to information about the user's current location obtained by the data provision terminal.

[0702] "Encryption" is the process of transforming data into a format that cannot be read by third parties in order to protect the confidentiality of the data.

[0703] A "secure communication protocol" is a network communication method designed to protect the confidentiality and integrity of data during transmission.

[0704] A "server" is a central computing device that receives data sent from terminals, processes it, and stores and manages it.

[0705] "Decryption" is the process of returning encrypted data to its original, readable format.

[0706] A "time-series data store" is a data management system for organizing and storing data based on time.

[0707] "Duplicate data" refers to information within a dataset where the same content is recorded multiple times.

[0708] "Incomplete data" refers to data that lacks necessary information, making it impossible to perform sufficient judgments or processing.

[0709] A "standardized format" is a set of criteria for organizing data recorded in different formats or units into a unified form.

[0710] A "clean dataset" is a collection of consistent and accurate data from which duplicates and errors have been removed.

[0711] A "data marketplace" is an online platform where data providers sell their data, and data buyers purchase and use that data.

[0712] A "custom dataset" is a collection of data that has been processed based on specific conditions or needs.

[0713] "Revenue sharing" is the process of fairly distributing the profits earned from data trading to data providers.

[0714] This invention provides a system that enables the efficient and secure collection, processing, and trading of location data. This system includes the following main components:

[0715] 1. Collection of location information by the device

[0716] Users grant permission to share their location information on their smartphones and other mobile devices. With the user's consent, the device periodically acquires location information using GPS and Wi-Fi signals. This allows for real-time tracking of the user's movements and current location. Furthermore, the location information is immediately encrypted using the AES encryption algorithm, ensuring the security of the information.

[0717] 2. Encryption and Data Transmission

[0718] The device sends encrypted location information to the server using a secure communication protocol (e.g., HTTPS). This process reduces the risk of eavesdropping or tampering during data transmission.

[0719] 3. Receiving and processing data by the server

[0720] The server decrypts the encrypted data received from the terminal and stores it in a time-series data store. This data store maintains data integrity and appropriately removes duplicate and incomplete data. As a result, a standardized, clean dataset is generated.

[0721] 4. Trading in the data market

[0722] Users (data buyers) can search for datasets tailored to their needs through an online platform. The server generates custom datasets in response to the data buyer's request and provides the necessary data. Buyers can purchase datasets through a secure online payment system and access them immediately. A specific example of a prompt might be, "I want to analyze the weekly visit patterns of visitors in a specific region. Please provide a suitable dataset."

[0723] This system creates an innovative platform that benefits both data providers and buyers, effectively providing the data infrastructure necessary for developing generative AI models.

[0724] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0725] Step 1:

[0726] The device obtains permission from the user to share its location. Upon receiving this input, the device acquires the user's location information using GPS or Wi-Fi. This location information is displayed on the electronic device in the form of latitude and longitude. The device then encrypts the acquired location information using the AES encryption algorithm. This encrypted data becomes the output.

[0727] Step 2:

[0728] The device sends encrypted location information to the server using a secure communication protocol (such as HTTPS). Using this protocol reduces the risk of third-party interception of the data during transmission. The output confirms that the encrypted data has safely reached the server.

[0729] Step 3:

[0730] The server receives encrypted data from the terminal as input. The server uses the decryption key to decrypt the data and restore it to its original location information. This decrypted location information is stored in a time-series data store. Here, duplicate and missing data are checked, and a clean dataset is output.

[0731] Step 4:

[0732] The server processes a clean dataset into a format suitable for the data market. Based on the request criteria from data buyers, it generates custom datasets filtered according to specific conditions. During this process, the dataset is evaluated to ensure it accurately matches the buyer's requirements, and a customized dataset is generated as output.

[0733] Step 5:

[0734] Users (data buyers) search for and purchase custom datasets on an online platform. Payment is made using a secure online payment system based on the user's selected dataset. An accessible download link is generated immediately after the purchase is complete.

[0735] Step 6:

[0736] The server calculates the transaction value obtained from the sale of datasets. Based on this transaction value, it distributes revenue to data providers. Revenue is calculated considering the amount of data provided and the frequency of use, and is transferred to the data provider's registered financial institution account. As an output, a fair revenue distribution is implemented.

[0737] (Application Example 1)

[0738] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0739] In large facilities such as logistics centers, there is a need to optimize the movement of mobile equipment such as robots and to manage them efficiently. However, currently, highly accurate optimization based on sufficient information is not being performed, which can lead to logistics congestion and unexpected delays. A system is needed to resolve this problem.

[0740] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0741] In this invention, the server includes a device for acquiring location information, a device for integrating the acquired location information and removing duplicate or incomplete data, and a device for optimizing logistics management using a generative AI model. This enables efficient optimization of the movement of mobile equipment in a logistics center and highly accurate logistics management.

[0742] A "location information acquisition device" is a device that has the function of detecting the user's location using GPS or Wi-Fi data in order to identify the user's travel route and place of stay.

[0743] A "device that integrates acquired location information and removes duplicate and incomplete data" is a device that aggregates location information obtained from multiple data sources and has the function of eliminating duplicate information and incomplete data in order to generate a consistent and clean dataset.

[0744] A "device for optimizing logistics management using generative AI models" is a system that generates algorithms and predictive models to optimize the movement and work efficiency of equipment and robots within a logistics center, based on aggregated location information.

[0745] A "device for distributing revenue to data providers according to transaction amount" is a system that has the function of distributing revenue obtained from selling acquired data in proportion to the transaction amount, in order to fairly return that revenue to data providers.

[0746] This invention is a system that links a server and terminals to optimize the movement of mobile equipment in a logistics center. The server is primarily responsible for data aggregation and analysis, while the terminals are responsible for collecting location information. Details of the embodiment are shown below.

[0747] The device periodically collects location information as the user or device moves. This location information is obtained using a GPS module or Wi-Fi, encrypted within the device, and sent to the server. The server receives the location information using a Python program or HTTP request library and records it in a database as real-time time-series data.

[0748] The server analyzes the received location information and removes duplicate and incomplete data. Advanced data processing techniques are used to ensure clean data in order to generate a standardized dataset. Furthermore, a generative AI model is used to predict patterns for optimizing movement routes within the logistics center, supporting route suggestions and efficiency improvements.

[0749] As a concrete example, consider an operational scenario in a large logistics center. By utilizing this system when logistics robots move goods at various locations, it becomes possible to avoid congested areas and set the most efficient routes. This improves delivery speed and increases the volume of goods handled per hour.

[0750] Example of a prompt:

[0751] "Please explain the method for generating models using location data to optimize robot movement within a logistics center."

[0752] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0753] Step 1:

[0754] The device acquires location information. When user movement is detected, the device uses its GPS module and Wi-Fi to obtain its current location. This information includes location coordinates and a timestamp. The acquired data is encrypted and prepared to be sent to the server.

[0755] Step 2:

[0756] The server receives location information. The server receives encrypted location data sent from the terminal and stores it in its database. At this time, the received data is recorded in real time and enters a waiting state for subsequent data processing.

[0757] Step 3:

[0758] The server performs data cleansing. It analyzes the received location data and removes duplicate and incomplete data. A data duplication checking algorithm is used to create a clean and consistent dataset. The output is an optimized standard dataset.

[0759] Step 4:

[0760] The server applies a generated AI model. The generated AI model, using a clean dataset as input, generates a model that predicts efficient robot movement within the logistics center. This allows for real-time proposal of the most likely optimal route, thereby improving movement efficiency.

[0761] Step 5:

[0762] The user receives optimized data and suggestions. The server presents the user with predicted optimal route information, which is then used in actual operations within the logistics center. Based on the information provided, the user adjusts the movement of robots and personnel to achieve efficient logistics management.

[0763] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0764] This invention is a system that recognizes user emotions and combines that data with location information to provide a richer dataset. This system enables the collection, integration, analysis, and provision of various types of data, and facilitates the development and utilization of generative AI models.

[0765] System Configuration

[0766] 1. Collection of emotional data and location information by devices

[0767] Users install an application that enables emotion recognition using their smartphones or wearable devices. The device analyzes the subject's voice, facial expressions, and biosignals, and acquires emotion data using an emotion engine. Simultaneously, it collects location information, packets both types of data, and sends them to the server.

[0768] 2. Data integration and processing by the server

[0769] The server stores sentiment data and location information received from multiple terminals in a time-series database. During integration, it detects data duplication and missing data and processes it to ensure it is stored in a standardized format. It also evaluates the relationship between sentiment data and location information and constructs the necessary datasets.

[0770] 3. Sales and provision of data on the market

[0771] Users (data buyers) can search for datasets related to specific emotional states or geographical locations through the online platform. Customized datasets, tailored to the buyer's requirements, are provided in an appropriate format and delivered via secure payment.

[0772] 4. Profit sharing

[0773] The server distributes revenue to data providers who provided emotional data and location information, based on the sales generated. Revenue is calculated and distributed fairly based on the utilization value of the data.

[0774] Specific example

[0775] Acquisition and utilization of emotional data

[0776] The device recognizes the user's emotions, such as joy or sadness, and also acquires their location information at that time. This data is used by marketing companies to analyze the effectiveness of advertisements.

[0777] Providing custom datasets

[0778] In a server-based service, if a retailer wants to know the sentiment trends of consumers in a specific area, the server generates and provides a dataset based on those conditions.

[0779] This invention maximizes the impact of location information with added emotional data on the generated AI model, enabling the provision of new business insights. Through this system, more advanced and context-sensitive data utilization is expected.

[0780] The following describes the processing flow.

[0781] Step 1:

[0782] The device uses an emotion recognition application installed on the user's device to acquire emotional data from the user's voice, facial expressions, and biosignals. The emotion engine analyzes this data in real time to identify the emotional state.

[0783] Step 2:

[0784] The device acquires emotional data and simultaneously collects current location information using its GPS function. The emotional data and location information are paired and encrypted as a single data packet.

[0785] Step 3:

[0786] The device sends encrypted data packets to the server via the internet connection specified by the user. This transmission uses protocols (e.g., HTTPS) that protect data accuracy and privacy.

[0787] Step 4:

[0788] The server analyzes the received data packets and stores sentiment data and location information in a time-series database. The data integration process removes duplicate data and detects and corrects missing data.

[0789] Step 5:

[0790] The server generates custom datasets linked to sentiment and location based on requests from buyers who use the data. This process involves filtering and aggregation to address specific marketing needs and analytical requests.

[0791] Step 6:

[0792] Users (data buyers) access the data through an online platform, select and purchase the custom datasets they need. The server conducts the transaction through a secure payment system and delivers the data to the buyer.

[0793] Step 7:

[0794] The server distributes revenue to data providers. It calculates the distribution based on the transaction amount and automatically transfers it to the data provider's registered account. This process is conducted fairly and transparently.

[0795] (Example 2)

[0796] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0797] In modern society, the importance of effectively integrating people's emotions and location information for commercial and research purposes is increasing. However, challenges remain, including inefficiencies from processing emotional and geographical data separately, the problem of duplicate data, and the difficulty of fairly distributing revenue to data providers. This makes it difficult to accurately utilize data and maintain the motivation of data providers.

[0798] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0799] In this invention, the server includes means for acquiring emotional data and location data from users, means for integrating the acquired emotional data and location data to remove duplicate and incomplete data, and means for making the integrated data available for development and utilization of generating AI models. This enables the effective integration of emotional information and location information to enhance commercial value while simultaneously allowing for fair revenue sharing to data providers.

[0800] A "user" refers to a person who provides emotional data and location data using this system.

[0801] "Emotional data" refers to information about a user's emotions obtained by analyzing their voice, facial expressions, and biosignals.

[0802] "Location data" refers to data that indicates a user's geographical location, and primarily refers to information obtained using sensor technologies such as GPS.

[0803] A "server" refers to a centralized computer system that receives, integrates, stores, analyzes, and provides data sent from terminals.

[0804] A "generative AI model" refers to an intelligent model developed using machine learning techniques to assist human tasks.

[0805] "Integration method" refers to the process by which the server combines the emotional and location data it receives, eliminating duplication and omissions, and converts it into a format that is easy to analyze.

[0806] A "revenue sharing mechanism" refers to a calculation and payment system that allows a server to fairly distribute revenue to data providers based on the amount of data traded.

[0807] A "dataset" refers to a collection of data organized based on specific criteria and used for analysis or trading.

[0808] This invention is a system that collects and integrates user emotion data and location data to provide it as a marketable dataset. Users install an emotion recognition application on a device such as a smartphone or wearable device. This application is designed using AI technology and is capable of voice recognition, facial recognition, and biosignal analysis. The device collects emotion data by analyzing the user's voice, facial expressions, and biosignals. Simultaneously, it acquires accurate location data using a GPS sensor.

[0809] The emotional and location data collected by the device are combined into a single packet and sent to the server using a secure communication protocol. The server stores the received data in a time-series database and performs integration and analysis. While checking for data duplication and missing information, it utilizes a generative AI model to evaluate the relationship between emotional and location information and generates a dataset that can be used for commercial and research purposes.

[0810] One concrete example of using this system is for marketing purposes. For instance, if a company is running an advertising campaign for a new product and wants to understand the emotional response of consumers in a specific region, the server can provide a dataset that matches those criteria.

[0811] Possible prompts for the generative AI model include: "Generate a dataset of users who are happy in a specific region and analyze how to maximize the effectiveness of advertising campaigns."

[0812] This system makes it possible to gain new business insights by effectively utilizing location data with added emotional information. A mechanism for fair and effective revenue sharing with data providers is also incorporated.

[0813] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0814] Step 1:

[0815] The user installs an emotion recognition app on their smartphone or wearable device. When the app is launched, it receives the user's voice, facial expressions, and biosignals as input data. The device analyzes this data using an AI model and outputs emotion data that identifies the user's emotional state.

[0816] Step 2:

[0817] The device simultaneously uses a GPS module to obtain precise location data. This location data indicates the user's current location and is packetized together with sentiment data. This packet contains information about the user's location and the emotions they are experiencing.

[0818] Step 3:

[0819] The device transmits acquired emotion and location data to the server via a secure communication protocol. The input here is data packets from the device, which the server receives and stores in time-series data storage.

[0820] Step 4:

[0821] The server analyzes the received data packets and performs a process to automatically clean up duplicate and incomplete data. The data processing performed by the server includes filtering to improve data accuracy. The output is an integrated, cleaned dataset.

[0822] Step 5:

[0823] The server uses a generative AI model to evaluate the relationship between sentiment data and location data. This processing step involves analyzing a cleaned dataset as input and generating a commercially usable dataset as output.

[0824] Step 6:

[0825] The server provides customized datasets to data buyers. When a user requests a specific dataset through the online platform, the server searches for data that matches the criteria and outputs it in the appropriate format.

[0826] Step 7:

[0827] The server distributes the revenue generated from the sale of data to the data providers. In this step, transaction revenue is used as input, and a fair revenue distribution is performed based on a pre-configured algorithm. The output is the fairly calculated reward.

[0828] (Application Example 2)

[0829] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0830] Traditional advertising delivery systems have the challenge of not adequately providing personalized advertising experiences that respond to users' emotions and circumstances. Furthermore, it has been difficult to efficiently integrate emotional data and location information and provide it as marketable data.

[0831] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0832] In this invention, the server includes means for acquiring emotional data from users, means for acquiring location information from data provision terminals, means for integrating the acquired emotional data and location information to remove duplicate and incomplete data, means for generating marketable information based on the integrated emotional data and location information, and means for presenting personalized data according to the conditions of the information purchaser. This makes it possible to provide an advertising experience that is tailored to the user's emotions and circumstances.

[0833] "Emotional data" refers to data acquired to represent a user's emotional state, and is obtained by analyzing voice, facial expressions, and biosignals.

[0834] "Location information" refers to geographical data used to determine the current location of a device, and is obtained using methods such as GPS.

[0835] "Integration" refers to the process of combining information obtained from multiple data sources into a single, standardized format, removing redundancies and missing data.

[0836] "Personalized data" refers to datasets that have been customized according to the specific conditions and requirements of the information purchaser.

[0837] "Marketable information" refers to information that integrates acquired sentiment data and location information in a commercially valuable form, making it available for buying and selling on the data market.

[0838] "Providing an advertising experience" means presenting the most relevant advertisements to users based on their emotional state and location information.

[0839] To implement this invention, a system is needed to collect user emotion data and integrate it with location information. Specifically, terminals such as smartphones and wearable devices are used, and emotion recognition applications are installed on these terminals. The emotion recognition application analyzes voice, facial expressions, and biosignals to generate user emotion data. Location information is obtained using GPS.

[0840] The device sends acquired emotion data and location information to the server. The server integrates this data using the Google Maps API and time-series databases. It checks for duplicate or missing data, and stores it in a standardized format. A generative AI model is used to select the most relevant advertisements for the user's situation from the integrated data.

[0841] The server delivers personalized advertisements to users in real time through the ad delivery network. For example, if a user expresses joy while in a specific commercial area, it can instantly deliver advertisements to that user that include discount information for nearby stores.

[0842] As a concrete example, the prompt "Select the most suitable advertisement when the user expresses surprise in a specific area. For example, consider a special invitation advertisement for a nearby new attraction." is used to perform data analysis and ad selection by a generative AI model.

[0843] This makes it possible to provide personalized advertising experiences that utilize emotional data and location information, enabling the delivery of information tailored to the user's situation.

[0844] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0845] Step 1:

[0846] The device analyzes the user's voice, facial expressions, and biosignals using an emotion recognition application to acquire emotional data. Inputs include voice, facial images, and biosignals, which are processed by an emotion analysis algorithm to output quantitative data representing the user's emotions, such as joy or surprise.

[0847] Step 2:

[0848] The device acquires location information from its GPS sensor along with the acquired emotional data. It receives emotional data and geographical coordinates as input, packets them into an integrated dataset, and sends it to the server. This integrated data links the user's emotional state with their location at that time.

[0849] Step 3:

[0850] The server uses the Google Maps API to match received sentiment data and location information with map data and stores it in a time-series database. The input consists of sentiment data and location information, which are converted into corresponding geographical information, checked for duplicates and missing data, and then output in a standardized format.

[0851] Step 4:

[0852] The server uses a generative AI model to select the most suitable advertisement based on the user's current situation from integrated data. Input consists of user data and prompt messages stored in a time-series database, and individual advertisement selection is performed using a machine learning algorithm. The output is the selected advertisement information.

[0853] Step 5:

[0854] The server sends selected advertisements to the device and displays them to the user. It utilizes an ad delivery network to provide real-time advertising. The input is advertising information, and the output is the display of advertisements on the user's device screen.

[0855] Step 6:

[0856] When a user responds to an advertisement (by clicking or taking action), the server collects this data and uses it to analyze the effectiveness of the advertisement and improve future selection algorithms. The input is user action data, and the output is the results of the advertisement effectiveness analysis.

[0857] 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.

[0858] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0859] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the robot 414.

[0860] 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.

[0861] Figure 9 shows an emotion map 400 in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0862] 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.

[0863] 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.

[0864] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, motorcycles, etc., emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0865] 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."

[0866] 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.

[0867] The above description primarily focuses on the functions of the data processing device 12 in relation to this disclosure. However, the system related to this disclosure is not necessarily implemented on a server. The system related to this disclosure may be implemented as a general information processing system. This disclosure may be implemented, for example, as a software program that runs on a personal computer or as an application that runs on a smartphone. The method related to this disclosure may be provided to users in SaaS (Software as a Service) format.

[0868] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing of the specific process may be performed by multiple computers, including computer 22. For example, a data generation model 58 may be provided in an external device of the data processing device 12, and the external device may generate data according to the input data.

[0869] 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.

[0870] 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.

[0871] 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.

[0872] 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.

[0873] 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.

[0874] 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.

[0875] 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.

[0876] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and the like that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0877] 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.

[0878] The following is further disclosed regarding the embodiments described above.

[0879] (Claim 1)

[0880] A means of obtaining location information from a data provisioning terminal,

[0881] A means of integrating acquired location information and removing duplicate or incomplete data,

[0882] A means of making integrated location information tradable in the market,

[0883] A system that includes a means of distributing revenue to data providers according to the transaction amount.

[0884] (Claim 2)

[0885] The system according to claim 1, comprising means for storing location information in a time-series database.

[0886] (Claim 3)

[0887] The system according to claim 1, comprising means for generating a custom dataset based on request conditions from data purchasers.

[0888] "Example 1"

[0889] (Claim 1)

[0890] A means of obtaining location information from a data provisioning terminal,

[0891] A means of encrypting the acquired location information and transmitting it to a server using a secure communication protocol,

[0892] A means for storing decrypted location information in a time-series data store,

[0893] A means for analyzing integrated location information, removing duplicate and incomplete data, and generating a clean dataset in a standardized format,

[0894] A means of providing data in a format tradable on the data market and generating custom datasets based on the request conditions of data buyers,

[0895] A system that includes a means of distributing revenue to data providers in proportion to the transaction amount from data trading.

[0896] (Claim 2)

[0897] The system according to claim 1, comprising means for organizing location information into a time-series database.

[0898] (Claim 3)

[0899] The system according to claim 1, comprising means for a data purchaser to search for a dataset via an online platform and purchase it using a secure online payment system.

[0900] "Application Example 1"

[0901] (Claim 1)

[0902] A device for acquiring location information,

[0903] A device that integrates acquired location information and removes duplicate and incomplete data,

[0904] A device that makes integrated location information tradable in the market,

[0905] A device for optimizing logistics management using a generative AI model,

[0906] A system that includes a device for distributing revenue to data providers according to the transaction amount.

[0907] (Claim 2)

[0908] The system according to claim 1, comprising a device for storing location information in a time-series data storage area.

[0909] (Claim 3)

[0910] The system according to claim 1, comprising a device that generates a custom data set based on request conditions from data purchasers.

[0911] "Example 2 of combining an emotion engine"

[0912] (Claim 1)

[0913] A means of obtaining emotional data and location data from users,

[0914] A means for integrating acquired emotional data and location data to remove duplicate and incomplete data,

[0915] A means to make integrated data available for the development and utilization of AI models,

[0916] A system that includes means for fairly distributing revenue to data providers based on the amount of data traded.

[0917] (Claim 2)

[0918] The system according to claim 1, comprising means for storing location data and sentiment data in a time-series data storage.

[0919] (Claim 3)

[0920] The system according to claim 1, comprising means for generating a custom dataset based on requirements from a data acquirer.

[0921] "Application example 2 when combining with an emotional engine"

[0922] (Claim 1)

[0923] A means of obtaining emotional data from users,

[0924] A means of obtaining location information from a data provisioning terminal,

[0925] A method for integrating acquired emotional data and location information to remove duplicate and incomplete data,

[0926] A means of generating market-tradable information based on integrated sentiment data and location information,

[0927] A means of presenting personalized data according to the information purchaser's criteria,

[0928] A system that includes a means of distributing revenue to data providers according to the transaction amount.

[0929] (Claim 2)

[0930] The system according to claim 1, comprising means for storing emotion data and location information in a time-series database.

[0931] (Claim 3)

[0932] The system according to claim 1, comprising means for generating a custom dataset to dynamically provide an individualized experience based on request conditions from information purchasers. [Explanation of Symbols]

[0933] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

1. A means of obtaining location information from a data provisioning terminal, A means of integrating acquired location information and removing duplicate or incomplete data, A means of making integrated location information tradable in the market, A system that includes a means of distributing revenue to data providers according to the transaction amount.

2. The system according to claim 1, comprising means for storing location information in a time-series database.

3. The system according to claim 1, comprising means for generating a custom dataset based on request conditions from data purchasers.

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