system

Abstract

The system according to this embodiment aims to efficiently advance the detailed digitalization of cities, facilitate the collection of data from a pedestrian's perspective, and support photography in locations where permission is required. [Solution] The system according to the embodiment comprises a collection unit, an organization unit, a generation unit, a reward unit, and a negotiation unit. The collection unit collects data. The organization unit organizes and uploads the data collected by the collection unit. The generation unit supplements any missing parts based on the data organized and uploaded by the organization unit. The reward unit provides rewards based on the data generated by the generation unit. The negotiation unit assists in negotiating locations where permission is required based on the rewards provided by the reward unit.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, there are problems that the detailed digitization of cities has not progressed, the real-time data collection from the perspective of pedestrians is scarce, and it is difficult to take pictures in places where permission is required.

[0005] The system according to the embodiment aims to efficiently promote the detailed digitization of cities, facilitate the data collection from the perspective of pedestrians, and support taking pictures in places where permission is required.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a collection unit, an organization unit, a generation unit, a reward unit, and a negotiation unit. The collection unit collects data. The organization unit organizes and uploads the data collected by the collection unit. The generation unit supplements any missing parts based on the data organized and uploaded by the organization unit. The reward unit provides rewards based on the data generated by the generation unit. The negotiation unit assists in negotiating locations where permission is required based on the rewards provided by the reward unit. [Effects of the Invention]

[0007] The system according to this embodiment can efficiently advance the detailed digitalization of cities, facilitate the collection of pedestrian-perspective data, and support filming in locations where permission is required. [Brief explanation of the drawing]

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

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

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

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

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

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

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

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

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

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

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

[0019] The smart device 14 comprises a computer 36, a 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.

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The urban digitalization system according to an embodiment of the present invention is a system for efficiently promoting the digitalization of cities. This urban digitalization system allows participants to collect video using a glasses-type device and organizes and uploads the data using a general-purpose data organization and upload method. Furthermore, it uses generative AI to supplement missing parts and create a seamless virtual space of the city. It also provides rewards according to the number of videos adopted and assists in negotiating locations where permission is required. First, the urban digitalization system allows participants to collect video using a glasses-type device. For example, a participant wears a glasses-type device while walking around the city and collects video in real time. This video is organized and uploaded using a general-purpose data organization and upload method. This allows for the rapid collection and updating of detailed data from a pedestrian's perspective. Next, the urban digitalization system uses generative AI to supplement missing parts. For example, if there are gaps in the collected video data, the generative AI fills in those gaps and creates a seamless virtual space of the city. This allows for the efficient promotion of urban digitalization. Furthermore, the urban digitalization system provides rewards according to the number of videos adopted. For example, if video collected by a participant is adopted, a reward is provided according to the number of videos. Furthermore, the urban digitalization system assists in negotiating permits for locations requiring them. For example, it helps in negotiating necessary permits for data collection in commercial and private areas. This system enables efficient urban digitalization and allows for the rapid collection and updating of detailed pedestrian-perspective data. In addition, participant-driven data collection allows for the construction of safe and comprehensive digital maps. Moreover, it promotes transparency and efficiency in data transactions targeting business areas, which is expected to create new business opportunities and revitalize local communities. In short, the urban digitalization system enables efficient urban digitalization.

[0029] The urban digitalization system according to this embodiment comprises a collection unit, an organization unit, a generation unit, a reward unit, and a negotiation unit. The collection unit collects data. The collection unit can, for example, collect video in real time using a glasses-type device. The organization unit organizes and uploads the data collected by the collection unit. The organization unit can, for example, organize and upload the data using a general-purpose data organization and upload method. The generation unit fills in any missing parts based on the data organized and uploaded by the organization unit. For example, if there are gaps in the collected video data, the generation AI can fill in those parts. The reward unit provides rewards based on the data generated by the generation unit. For example, the reward unit can provide rewards according to the number of videos used. The negotiation unit assists in negotiating for locations requiring permission based on the rewards provided by the reward unit. For example, the negotiation unit can assist in negotiating to obtain necessary permission for data collection in commercial or private areas. Thus, the urban digitalization system according to this embodiment can efficiently perform data collection, organization, generation, reward provision, and negotiation support. Some or all of the above-mentioned processes in the collection, organization, generation, reward, and negotiation units may be performed using AI, for example, or without AI. For example, the collection unit can input video data collected by a glasses-type device into a generation AI, which can then organize and supplement the data.

[0030] The data collection unit collects data. For example, the data collection unit can collect video in real time using a glasses-type device. Specifically, the glasses-type device is equipped with a high-resolution camera that can record the user's field of view as video data. This device is lightweight, easy to carry, and has a battery that can withstand long-term use. In addition to glasses-type devices, the data collection unit can also collect data using devices such as drones, fixed cameras, and smartphones. Drones are suitable for collecting wide-area video data from the air, and fixed cameras are suitable for long-term monitoring of specific locations. Smartphones are suitable for data collection while on the go because users carry them with them. These devices can transmit the collected data to a cloud server in real time and manage it centrally. The data collection unit can integrate the diverse data obtained from these devices and efficiently collect the information necessary for the digitalization of cities. Furthermore, by adjusting the data collection frequency and resolution, the data collection unit can flexibly collect data according to specific situations and needs. For example, during specific events or disasters, the collection frequency can be increased to collect detailed data, and during normal times, the collection frequency can be decreased to reduce the amount of data. This allows the data collection unit to efficiently and effectively collect data and play a supporting role in the foundation of the urban digitalization system.

[0031] The Sorting Department organizes and uploads the data collected by the Collection Department. The Sorting Department can organize and upload data using, for example, general-purpose data sorting and uploading methods. Specifically, it classifies collected video data by time and location, and adds metadata to facilitate management. Metadata includes information such as the date and time of collection, location, and type of collection device. This allows subsequent processing departments to utilize the data efficiently. The Sorting Department also performs filtering to remove data duplication and noise and improve quality. For example, it automatically detects and deletes unnecessary or low-quality parts of video data. Furthermore, data uploads are protected by security measures, including data encryption and access control. This ensures the confidentiality and security of the data. The Sorting Department can also automate data sorting and uploading using AI. For example, AI analyzes the collected data and automatically determines the optimal classification method and filtering conditions. This improves the efficiency of data sorting and reduces the burden of human work. Additionally, the Sorting Department can flexibly select the data upload destination. For example, the optimal upload destination can be selected according to the purpose of use and security requirements, such as cloud storage or on-premises servers. This allows the data organization department to efficiently organize and upload the collected data, supporting data management across the entire system.

[0032] The generation unit fills in missing parts based on the data organized and uploaded by the organization unit. For example, if there are gaps in the collected video data, the generation AI can fill in those gaps. Specifically, the generation AI analyzes existing video data and generates video suitable for the missing parts. Using deep learning technology, the generation AI can learn patterns and features of video and generate highly accurate supplementary video. For example, if there are gaps in road video data, the generation AI will fill in the road video in a natural way based on surrounding video data. In addition to video data, the generation AI can also supplement audio data and sensor data. For example, if audio data contains noise, the generation AI will remove the noise and generate clear audio. If there are gaps in sensor data, the generation AI will fill in the missing parts based on other sensor data. This allows the generation unit to maintain data integrity and quality and improve the reliability of the urban digitalization system. Furthermore, the generation unit regularly updates the generation AI's training data to achieve highly accurate supplementation based on the latest data. This allows the generation unit to always provide high-quality data and improve the overall system performance.

[0033] The rewards department provides rewards based on the data generated by the generation department. For example, the rewards department can provide rewards based on the number of videos that are selected. Specifically, if the collected video data is organized, generated, and ultimately selected, the user who provided the data will be paid a reward. The reward calculation takes into account the quality and importance of the data, as well as the difficulty of collection. For example, high-resolution video data of important locations will be rewarded more highly. The rewards department can also flexibly choose the method of payment for rewards. For example, it can provide rewards according to the user's preference, such as cash, electronic money, or points. The rewards department can also use AI to automate the calculation and payment of rewards. For example, AI can automatically evaluate the quality and importance of the collected data and calculate the optimal reward amount. This ensures that rewards are calculated quickly and accurately, improving user satisfaction. Furthermore, the rewards department manages the reward history, allowing users to check their reward status. This allows users to understand their contribution and reward status, and maintain their motivation. In this way, the rewards department can provide users with appropriate rewards and encourage their cooperation in data collection.

[0034] The Negotiation Department assists in negotiating for permits in locations requiring them, based on the compensation provided by the Compensation Department. For example, the Negotiation Department can assist in negotiating for necessary permits for data collection in commercial and private areas. Specifically, the Negotiation Department negotiates to gain the understanding and cooperation of stakeholders by explaining the purpose and methods of data collection, as well as how the collected data will be used. The Negotiation Department can utilize AI to simulate negotiations and propose optimal negotiation strategies. For example, the AI ​​analyzes past negotiation data to learn successful negotiation patterns and effective negotiation methods, enabling the Negotiation Department to conduct more effective negotiations. Furthermore, the Negotiation Department manages the progress and results of negotiations in real time and takes prompt action as needed. For example, if negotiations are stalled, it can report to higher-level managers and request additional support. In addition, the Negotiation Department can record negotiation results in a database for use in future negotiations. This allows the Negotiation Department to smoothly conduct data collection in locations requiring permits and expand the data collection scope of the urban digitalization system.

[0035] The collection unit can collect video in real time using a glasses-type device. The collection unit can, for example, collect video in real time using a glasses-type device. The collection unit can, for example, collect video while a participant wearing a glasses-type device walks around town. The collection unit can, for example, collect high-resolution video using a glasses-type device. This allows the collection unit to collect video in real time by using a glasses-type device. The glasses-type device includes, for example, camera resolution, communication functions, etc., but is not limited to such examples. Some or all of the processing described above in the collection unit may be performed using, for example, AI, or not using AI. For example, the collection unit can input video data collected by the glasses-type device into a generating AI, and the generating AI can organize and supplement the data.

[0036] The data organization unit can organize and upload data using general-purpose data organization and upload methods. For example, the data organization unit can organize and upload data using general-purpose data organization and upload methods. For example, the data organization unit can organize data using data classification methods and organization algorithms. For example, the data organization unit can upload data using a destination server and upload protocol. This allows the data organization unit to efficiently organize and upload data using general-purpose methods. General-purpose data organization and upload methods include, but are not limited to, the software used and organization algorithms. Some or all of the above-described processes in the data organization unit may be performed using, for example, AI, or not using AI. For example, the data organization unit can input data collected by the data collection unit into a generating AI, and the generating AI can organize and upload the data.

[0037] The generation unit can use the generation AI to fill in any missing parts in the collected video data. For example, if there are missing parts in the collected video data, the generation AI can fill in those parts. The generation unit can use the generation AI to fill in the missing parts and create a seamless virtual city space. For example, if there are missing parts in the collected video data, the generation AI can fill in those parts and provide seamless data. Thus, the generation unit can use the generation AI to fill in the missing parts and provide seamless data. The generation AI includes, but is not limited to, the algorithm used and the type of training data. Some or all of the above processing in the generation unit may be performed using AI or not. For example, the generation unit can input data collected by the collection unit into the generation AI, and the generation AI can fill in the data.

[0038] The rewards unit can provide rewards according to the number of videos selected. The rewards unit can, for example, provide rewards according to the number of videos selected to increase participant motivation. The rewards unit can, for example, provide rewards according to the number of videos selected to increase participant motivation. This allows the rewards unit to increase participant motivation by providing rewards according to the number of videos selected. The number of videos selected includes, but is not limited to, the evaluation criteria for selection and the selection process. Some or all of the above processing in the rewards unit may be performed using, for example, AI, or not using AI. For example, the rewards unit can input the data generated in the generation unit into a generation AI, and the generation AI can provide rewards.

[0039] The Negotiation Department can assist in negotiating to obtain necessary permits for data collection in commercial and private areas. The Negotiation Department can assist in negotiating to obtain necessary permits for data collection in commercial and private areas, for example. The Negotiation Department can assist in negotiating to obtain necessary permits for data collection in commercial and private areas, for example, to ensure that data collection proceeds smoothly. The Negotiation Department can assist in negotiating to obtain necessary permits for data collection in commercial and private areas, for example, to ensure that data collection proceeds smoothly. By assisting in negotiating to obtain necessary permits, the Negotiation Department can ensure that data collection proceeds smoothly. Commercial and private areas include, but are not limited to, commercial facilities and private residences. Some or all of the above processing in the Negotiation Department may be performed using, for example, AI, or not using AI. For example, the Negotiation Department can input the rewards provided by the Reward Department into a Generating AI, and the Generating AI can assist in negotiations.

[0040] The collection unit can analyze the user's past video collection history and select the optimal collection method. For example, the collection unit can analyze the user's past video collection history and select the optimal collection method. For example, the collection unit can analyze the patterns of videos previously collected by the user and perform collection using similar patterns. For example, the collection unit can consider the time periods of videos previously collected by the user and perform collection at the same time periods. For example, the collection unit can refer to the locations of videos previously collected by the user and perform collection at the same locations. In this way, the collection unit can select the optimal collection method by analyzing past collection history. The optimal collection method includes, but is not limited to, the timing of collection and the means of collection. Some or all of the above processing in the collection unit may be performed using, for example, AI, or not using AI. For example, the collection unit can input the user's past video collection history into a generating AI, and the generating AI can select the optimal collection method.

[0041] The collection unit can filter video footage based on the user's current activities and areas of interest. For example, the collection unit can prioritize collecting video footage of tourist attractions if the user is sightseeing, or of commercial areas if the user is shopping, or of commuting if the user is commuting. By filtering based on the user's activities and areas of interest, the collection unit can collect highly relevant video footage. Filtering includes, but is not limited to, filtering conditions and algorithms used. Some or all of the processing described above in the collection unit may be performed using, for example, AI, or not. For example, the collection unit can input the user's activities and areas of interest into a generating AI, which can then perform the filtering.

[0042] The collection unit can prioritize the collection of highly relevant videos by considering the user's geographical location information during video collection. For example, if the user is in a tourist area, the collection unit can prioritize the collection of videos of major spots in that tourist area. For example, if the user is in a commercial area, the collection unit can prioritize the collection of videos of shops and facilities in that area. For example, if the user is in a residential area, the collection unit can prioritize the collection of videos of the living environment in that area. In this way, the collection unit can prioritize the collection of highly relevant videos by considering geographical location information. Geographical location information includes, but is not limited to, GPS data and methods for analyzing location information. Some or all of the above processing in the collection unit may be performed using, for example, AI, or without AI. For example, the collection unit can input the user's geographical location information into a generating AI, and the generating AI can prioritize the collection of highly relevant videos.

[0043] The collection unit can analyze the user's social media activity and collect relevant videos when collecting video. For example, the collection unit can analyze the user's social media activity and collect relevant videos when collecting video. For example, the collection unit can prioritize collecting videos of places the user has shared on social media. For example, the collection unit can prioritize collecting videos of events the user has shown interest in on social media. For example, the collection unit can prioritize collecting videos of places related to accounts the user follows on social media. In this way, the collection unit can collect relevant videos by analyzing social media activity. Social media activity includes, but is not limited to, analysis of posted content and analysis of activity history. Some or all of the above processing in the collection unit may be performed using, for example, AI, or not using AI. For example, the collection unit can input the user's social media activity into a generating AI, and the generating AI can collect relevant videos.

[0044] The sorting unit can adjust the level of detail in sorting based on the importance of the collected data during sorting. For example, the sorting unit can sort important data in detail and prioritize its upload. For example, it can sort general data in a simplified manner and postpone it. For example, it can delete unnecessary data to improve sorting efficiency. In this way, the sorting unit can perform efficient data sorting by adjusting the level of detail in sorting based on the importance of the data. Data importance includes, but is not limited to, data value and data usage frequency. Some or all of the above processing in the sorting unit may be performed using, for example, AI, or not using AI. For example, the sorting unit can input the importance of the collected data into a generating AI, and the generating AI can adjust the level of detail in sorting.

[0045] The data sorting unit can apply different sorting algorithms depending on the data category when sorting data. For example, the sorting unit can classify tourist destination data into the tourism category and apply a dedicated sorting algorithm. For example, the sorting unit can classify commercial area data into the commercial category and apply a dedicated sorting algorithm. For example, the sorting unit can classify residential area data into the residential category and apply a dedicated sorting algorithm. This allows the sorting unit to perform efficient data sorting by applying sorting algorithms according to the data category. Data categories include, but are not limited to, data type and data use. Some or all of the above processing in the sorting unit may be performed using, for example, AI, or not using AI. For example, the sorting unit can input data categories into a generating AI, and the generating AI can apply different sorting algorithms.

[0046] The sorting unit can adjust the sorting order based on the data collection date during data sorting. For example, the sorting unit can sort the data based on the data collection date during data sorting. The sorting unit can, for example, prioritize sorting the latest data and upload it quickly. The sorting unit can, for example, sort older data later. The sorting unit can, for example, prioritize sorting data collected during a specific event period. This allows the sorting unit to perform efficient data sorting by adjusting the sorting order based on the data collection date. The data collection date includes, but is not limited to, recording the collection date and time, and methods for analyzing the collection date. Some or all of the above processing in the sorting unit may be performed using, for example, AI, or not using AI. For example, the sorting unit can input the data collection date into a generating AI, and the generating AI can adjust the sorting order.

[0047] The data organization unit can adjust its organization method based on the relationships between data when organizing data. For example, the data organization unit can organize highly relevant data together and upload them efficiently. For example, the data organization unit can organize less relevant data individually and postpone processing it. For example, the data organization unit can analyze the relationships between data and apply the optimal organization method. This allows the data organization unit to perform efficient data organization by adjusting its organization method based on the relationships between data. The relationships between data include, but are not limited to, data correlations and methods for analyzing relationships. Some or all of the above processing in the data organization unit may be performed using, for example, AI, or not using AI. For example, the data organization unit can input the relationships between data into a generating AI, and the generating AI can adjust the organization method.

[0048] The generation unit can adjust the level of detail of the generated data based on the importance of the missing data during generation. For example, the generation unit can adjust the level of detail of the generated data based on the importance of the missing data during generation. For example, the generation unit can generate important missing data in detail to provide seamless data. For example, the generation unit can generate general missing data in a simplified manner. For example, the generation unit can improve efficiency by not generating unnecessary missing data. In this way, the generation unit can perform efficient data generation by adjusting the level of detail of the generated data based on the importance of the missing data. The importance of the missing data includes, but is not limited to, the degree of impact of the missing data and the criteria for evaluating importance. Some or all of the above processing in the generation unit may be performed using, for example, AI, or not using AI. For example, the generation unit can input the importance of the missing data into the generation AI, and the generation AI can adjust the level of detail of the generated data.

[0049] The generation unit can apply different generation algorithms depending on the data category during generation. For example, the generation unit can classify tourist destination data into the tourism category and apply a dedicated generation algorithm. For example, the generation unit can classify commercial area data into the commercial category and apply a dedicated generation algorithm. For example, the generation unit can classify residential area data into the residential category and apply a dedicated generation algorithm. This allows the generation unit to efficiently generate data by applying a generation algorithm according to the data category. The generation algorithm includes, but is not limited to, the type of algorithm used and the method of implementing the algorithm. Some or all of the above-described processes in the generation unit may be performed using, for example, AI, or without AI. For example, the generation unit can input the data category into a generation AI, and the generation AI can apply different generation algorithms.

[0050] The generation unit can adjust the generation order based on the data collection timing during generation. For example, the generation unit can adjust the generation order based on the data collection timing during generation. The generation unit can, for example, prioritize the generation of the latest data and provide it quickly. The generation unit can, for example, postpone the generation of older data. The generation unit can, for example, prioritize the generation of data collected during a specific event period. This allows the generation unit to perform efficient data generation by adjusting the generation order based on the data collection timing. The data collection timing includes, but is not limited to, recording the date and time of collection and methods for analyzing the collection timing. Some or all of the above-described processes in the generation unit may be performed using, for example, AI, or not using AI. For example, the generation unit can input the data collection timing into a generation AI, and the generation AI can adjust the generation order.

[0051] The generation unit can adjust its generation method based on the relevance of the data during generation. For example, the generation unit can adjust its generation method based on the relevance of the data during generation. For example, the generation unit can generate highly relevant data together and provide it efficiently. For example, the generation unit can generate less relevant data individually and postpone it. For example, the generation unit can analyze the relevance of the data and apply the optimal generation method. In this way, the generation unit can perform efficient data generation by adjusting its generation method based on the relevance of the data. The relevance of the data includes, but is not limited to, data correlation and methods for analyzing relevance. Some or all of the above-described processes in the generation unit may be performed using, for example, AI, or not using AI. For example, the generation unit can input the relevance of the data into a generation AI, and the generation AI can adjust the generation method.

[0052] The rewards unit can adjust the level of detail of rewards based on the importance of the collected footage when providing rewards. For example, the rewards unit can adjust the level of detail of rewards based on the importance of the collected footage when providing rewards. For example, the rewards unit can provide high rewards for important footage. For example, the rewards unit can provide standard rewards for general footage. For example, the rewards unit can not provide rewards for unnecessary footage. This allows the rewards unit to provide rewards efficiently by adjusting the level of detail of rewards based on the importance of the collected footage. The importance of the collected footage includes, but is not limited to, the value of the footage or the frequency of use of the footage. Some or all of the processing described above in the rewards unit may be performed using, for example, AI, or not using AI. For example, the rewards unit can input the importance of the collected footage into a generating AI, and the generating AI can adjust the level of detail of the rewards.

[0053] The reward unit can apply different reward algorithms depending on the video category when providing rewards. For example, the reward unit can apply a different reward algorithm depending on the video category when providing rewards. For example, the reward unit can apply a reward algorithm for the tourism category to videos of tourist destinations. For example, the reward unit can apply a reward algorithm for the commercial category to videos of commercial areas. For example, the reward unit can apply a reward algorithm for the residential category to videos of residential areas. This allows the reward unit to provide rewards efficiently by applying a reward algorithm according to the video category. The reward algorithm includes, but is not limited to, the type of algorithm used and the method of implementing the algorithm. Some or all of the above processing in the reward unit may be performed using, for example, AI, or not using AI. For example, the reward unit can input the video category into a generating AI, and the generating AI can apply different reward algorithms.

[0054] The rewards unit can adjust the order of rewards based on the video collection date when providing rewards. For example, the rewards unit can adjust the order of rewards based on the video collection date when providing rewards. For example, the rewards unit can prioritize rewards for the most recent videos. For example, the rewards unit can postpone rewards for older videos. For example, the rewards unit can prioritize rewards for videos collected during a specific event period. This allows the rewards unit to provide rewards efficiently by adjusting the order of rewards based on the video collection date. The video collection date includes, but is not limited to, recording the collection date and time, and methods for analyzing the collection date. Some or all of the above processing in the rewards unit may be performed using, for example, AI, or not using AI. For example, the rewards unit can input the video collection date into a generating AI, and the generating AI can adjust the order of rewards.

[0055] The reward unit can adjust the reward method based on the relevance of the videos when providing rewards. For example, the reward unit can adjust the reward method based on the relevance of the videos when providing rewards. For example, the reward unit can provide high rewards for highly relevant videos. For example, the reward unit can provide standard rewards for less relevant videos. For example, the reward unit can not provide rewards for unnecessary videos. In this way, the reward unit can provide rewards efficiently by adjusting the reward method based on the relevance of the videos. Video relevance includes, but is not limited to, video correlations and methods for analyzing relevance. Some or all of the above processing in the reward unit may be performed using, for example, AI, or not using AI. For example, the reward unit can input the relevance of the videos into a generating AI, and the generating AI can adjust the reward method.

[0056] The negotiating unit can adjust the level of detail in negotiations based on the importance of the collected data. For example, the negotiating unit can adjust the level of detail in negotiations based on the importance of the collected data. For example, the negotiating unit can conduct detailed negotiations for important data. For example, the negotiating unit can conduct simplified negotiations for general data. For example, the negotiating unit can not negotiate for unnecessary data. This allows the negotiating unit to conduct efficient negotiations by adjusting the level of detail in negotiations based on the importance of the collected data. The importance of the collected data includes, but is not limited to, data value and data usage frequency. Some or all of the above processing in the negotiating unit may be performed using, for example, AI, or not using AI. For example, the negotiating unit can input the importance of the collected data into a generating AI, and the generating AI can adjust the level of detail in negotiations.

[0057] The negotiation unit can apply different negotiation algorithms depending on the data category during negotiations. For example, the negotiation unit can apply a negotiation algorithm for the tourism category to tourist destination data. For example, the negotiation unit can apply a negotiation algorithm for the commercial category to commercial area data. For example, the negotiation unit can apply a negotiation algorithm for the residential area data. This allows the negotiation unit to conduct efficient negotiations by applying a negotiation algorithm according to the data category. Negotiation algorithms include, but are not limited to, the type of algorithm used and the method of implementing the algorithm. Some or all of the above-described processes in the negotiation unit may be performed using, for example, AI, or not using AI. For example, the negotiation unit can input the data category into a generating AI, and the generating AI can apply different negotiation algorithms.

[0058] The negotiating unit can adjust the order of negotiations based on the timing of data collection during negotiations. For example, the negotiating unit can prioritize negotiations on the most recent data. For example, the negotiating unit can postpone negotiations on older data. For example, the negotiating unit can prioritize negotiations on data collected during a specific event period. This allows the negotiating unit to conduct efficient negotiations by adjusting the order of negotiations based on the timing of data collection. The timing of data collection includes, but is not limited to, recording the date and time of collection and methods for analyzing the timing of collection. Some or all of the above processing in the negotiating unit may be performed using, for example, AI, or not using AI. For example, the negotiating unit can input the timing of data collection into a generating AI, and the generating AI can adjust the order of negotiations.

[0059] The negotiating unit can adjust its negotiation methods based on the relevance of the data during negotiations. For example, the negotiating unit can adjust its negotiation methods based on the relevance of the data during negotiations. For example, the negotiating unit can conduct detailed negotiations for highly relevant data. For example, the negotiating unit can conduct simplified negotiations for less relevant data. For example, the negotiating unit can refrain from negotiating for unnecessary data. This allows the negotiating unit to conduct efficient negotiations by adjusting its negotiation methods based on the relevance of the data. The relevance of the data includes, but is not limited to, data correlations and methods for analyzing relevances. Some or all of the above processing in the negotiating unit may be performed using, for example, AI, or not using AI. For example, the negotiating unit can input the relevance of the data into a generating AI, and the generating AI can adjust the negotiation method.

[0060] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0061] The data sorting unit can adjust the level of detail in the sorting process based on the importance of the collected data. For example, important data can be sorted in detail and uploaded preferentially. General data can be sorted simply and postponed. Unnecessary data can be deleted to improve the efficiency of the sorting process. In this way, the data sorting unit can perform efficient data sorting by adjusting the level of detail in the sorting process based on the importance of the data. Data importance includes, but is not limited to, data value and data usage frequency. Some or all of the above processing in the data sorting unit may be performed using, for example, AI, or not using AI. For example, the data sorting unit can input the importance of the collected data into a generating AI, and the generating AI can adjust the level of detail in the sorting process.

[0062] The generation unit can adjust the level of detail of the generated data based on the importance of the missing data. For example, important missing data can be generated in detail to provide seamless data. General missing data can be generated in a simplified manner. Unnecessary missing data can be omitted to improve efficiency. In this way, the generation unit can perform efficient data generation by adjusting the level of detail of the generated data based on the importance of the missing data. The importance of the missing data includes, but is not limited to, the degree of impact of the missing data and the criteria for evaluating importance. Some or all of the above processing in the generation unit may be performed using AI, for example, or not using AI. For example, the generation unit can input the importance of the missing data into the generation AI, and the generation AI can adjust the level of detail of the generated data.

[0063] The collection unit can analyze the user's past video collection history and select the optimal collection method. For example, it can analyze patterns in videos previously collected by the user and collect videos using similar patterns. It can consider the time periods of videos previously collected by the user and collect videos at the same time periods. It can refer to the locations of videos previously collected by the user and collect videos at the same locations. In this way, the collection unit can select the optimal collection method by analyzing past collection history. The optimal collection method includes, but is not limited to, the timing of collection and the means of collection. Some or all of the above processing in the collection unit may be performed using AI, for example, or without AI. For example, the collection unit can input the user's past video collection history into a generating AI, and the generating AI can select the optimal collection method.

[0064] The collection unit can filter video footage based on the user's current activities and areas of interest. For example, if the user is sightseeing, it can prioritize collecting footage of tourist attractions. If the user is shopping, it can prioritize collecting footage of commercial areas. If the user is commuting, it can prioritize collecting footage of their commute route. This allows the collection unit to collect highly relevant footage by filtering based on the user's activities and areas of interest. Filtering includes, but is not limited to, filtering conditions and the algorithms used. Some or all of the processing described above in the collection unit may be performed using AI, for example, or without AI. For example, the collection unit can input the user's activities and areas of interest into a generating AI, which can then perform the filtering.

[0065] The data sorting unit can apply different sorting algorithms depending on the data category during data sorting. For example, data for tourist destinations can be classified into the tourism category and a dedicated sorting algorithm can be applied. Data for commercial areas can be classified into the commercial category and a dedicated sorting algorithm can be applied. Data for residential areas can be classified into the residential category and a dedicated sorting algorithm can be applied. This allows the data sorting unit to perform efficient data sorting by applying sorting algorithms according to the data category. Data categories include, but are not limited to, data type and data use. Some or all of the above processing in the data sorting unit may be performed using, for example, AI, or not using AI. For example, the data sorting unit can input data categories into a generating AI, and the generating AI can apply different sorting algorithms.

[0066] The generation unit can adjust the generation order based on the data collection timing during generation. For example, it can prioritize generating the latest data and provide it quickly. Older data can be generated later. Data collected during a specific event period can be prioritized. This allows the generation unit to efficiently generate data by adjusting the generation order based on the data collection timing. The data collection timing includes, but is not limited to, recording the collection date and time, and methods for analyzing the collection timing. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input the data collection timing into a generation AI, and the generation AI can adjust the generation order.

[0067] The negotiation department can adjust its negotiation methods based on the relevance of the data during negotiations. For example, it can conduct detailed negotiations for highly relevant data, simplified negotiations for less relevant data, and no negotiations for unnecessary data. This allows the negotiation department to conduct efficient negotiations by adjusting its methods based on the relevance of the data. Data relevance includes, but is not limited to, data correlations and methods of analyzing relevance. Some or all of the above-described processes in the negotiation department may be performed using AI, for example, or not using AI. For example, the negotiation department can input data relevances into a generative AI, which can then adjust the negotiation method.

[0068] The following briefly describes the processing flow for example form 1.

[0069] Step 1: The data collection unit collects data. For example, video can be collected in real time using a glasses-type device. Step 2: The organization unit organizes and uploads the data collected by the collection unit. For example, the data can be organized and uploaded using a general-purpose data organization and upload method. Step 3: The generation unit fills in any missing parts based on the data organized and uploaded by the organization unit. For example, if there are gaps in the collected video data, the generation AI can fill in those gaps. Step 4: The reward unit provides rewards based on the data generated by the generation unit. For example, rewards can be provided based on the number of videos selected. Step 5: The Negotiation Department assists in negotiating for necessary permits based on the compensation provided by the Compensation Department. For example, they can assist in negotiating for necessary permits for data collection in commercial or private areas.

[0070] (Example of form 2) The urban digitalization system according to an embodiment of the present invention is a system for efficiently promoting the digitalization of cities. This urban digitalization system allows participants to collect video using a glasses-type device and organizes and uploads the data using a general-purpose data organization and upload method. Furthermore, it uses generative AI to supplement missing parts and create a seamless virtual space of the city. It also provides rewards according to the number of videos adopted and assists in negotiating locations where permission is required. First, the urban digitalization system allows participants to collect video using a glasses-type device. For example, a participant wears a glasses-type device while walking around the city and collects video in real time. This video is organized and uploaded using a general-purpose data organization and upload method. This allows for the rapid collection and updating of detailed data from a pedestrian's perspective. Next, the urban digitalization system uses generative AI to supplement missing parts. For example, if there are gaps in the collected video data, the generative AI fills in those gaps and creates a seamless virtual space of the city. This allows for the efficient promotion of urban digitalization. Furthermore, the urban digitalization system provides rewards according to the number of videos adopted. For example, if video collected by a participant is adopted, a reward is provided according to the number of videos. Furthermore, the urban digitalization system assists in negotiating permits for locations requiring them. For example, it helps in negotiating necessary permits for data collection in commercial and private areas. This system enables efficient urban digitalization and allows for the rapid collection and updating of detailed pedestrian-perspective data. In addition, participant-driven data collection allows for the construction of safe and comprehensive digital maps. Moreover, it promotes transparency and efficiency in data transactions targeting business areas, which is expected to create new business opportunities and revitalize local communities. In short, the urban digitalization system enables efficient urban digitalization.

[0071] The urban digitalization system according to this embodiment comprises a collection unit, an organization unit, a generation unit, a reward unit, and a negotiation unit. The collection unit collects data. The collection unit can, for example, collect video in real time using a glasses-type device. The organization unit organizes and uploads the data collected by the collection unit. The organization unit can, for example, organize and upload the data using a general-purpose data organization and upload method. The generation unit fills in any missing parts based on the data organized and uploaded by the organization unit. For example, if there are gaps in the collected video data, the generation AI can fill in those parts. The reward unit provides rewards based on the data generated by the generation unit. For example, the reward unit can provide rewards according to the number of videos used. The negotiation unit assists in negotiating for locations requiring permission based on the rewards provided by the reward unit. For example, the negotiation unit can assist in negotiating to obtain necessary permission for data collection in commercial or private areas. Thus, the urban digitalization system according to this embodiment can efficiently perform data collection, organization, generation, reward provision, and negotiation support. Some or all of the above-mentioned processes in the collection, organization, generation, reward, and negotiation units may be performed using AI, for example, or without AI. For example, the collection unit can input video data collected by a glasses-type device into a generation AI, which can then organize and supplement the data.

[0072] The data collection unit collects data. For example, the data collection unit can collect video in real time using a glasses-type device. Specifically, the glasses-type device is equipped with a high-resolution camera that can record the user's field of view as video data. This device is lightweight, easy to carry, and has a battery that can withstand long-term use. In addition to glasses-type devices, the data collection unit can also collect data using devices such as drones, fixed cameras, and smartphones. Drones are suitable for collecting wide-area video data from the air, and fixed cameras are suitable for long-term monitoring of specific locations. Smartphones are suitable for data collection while on the go because users carry them with them. These devices can transmit the collected data to a cloud server in real time and manage it centrally. The data collection unit can integrate the diverse data obtained from these devices and efficiently collect the information necessary for the digitalization of cities. Furthermore, by adjusting the data collection frequency and resolution, the data collection unit can flexibly collect data according to specific situations and needs. For example, during specific events or disasters, the collection frequency can be increased to collect detailed data, and during normal times, the collection frequency can be decreased to reduce the amount of data. This allows the data collection unit to efficiently and effectively collect data and play a supporting role in the foundation of the urban digitalization system.

[0073] The Sorting Department organizes and uploads the data collected by the Collection Department. The Sorting Department can organize and upload data using, for example, general-purpose data sorting and uploading methods. Specifically, it classifies collected video data by time and location, and adds metadata to facilitate management. Metadata includes information such as the date and time of collection, location, and type of collection device. This allows subsequent processing departments to utilize the data efficiently. The Sorting Department also performs filtering to remove data duplication and noise and improve quality. For example, it automatically detects and deletes unnecessary or low-quality parts of video data. Furthermore, data uploads are protected by security measures, including data encryption and access control. This ensures the confidentiality and security of the data. The Sorting Department can also automate data sorting and uploading using AI. For example, AI analyzes the collected data and automatically determines the optimal classification method and filtering conditions. This improves the efficiency of data sorting and reduces the burden of human work. Additionally, the Sorting Department can flexibly select the data upload destination. For example, the optimal upload destination can be selected according to the purpose of use and security requirements, such as cloud storage or on-premises servers. This allows the data organization department to efficiently organize and upload the collected data, supporting data management across the entire system.

[0074] The generation unit fills in missing parts based on the data organized and uploaded by the organization unit. For example, if there are gaps in the collected video data, the generation AI can fill in those gaps. Specifically, the generation AI analyzes existing video data and generates video suitable for the missing parts. Using deep learning technology, the generation AI can learn patterns and features of video and generate highly accurate supplementary video. For example, if there are gaps in road video data, the generation AI will fill in the road video in a natural way based on surrounding video data. In addition to video data, the generation AI can also supplement audio data and sensor data. For example, if audio data contains noise, the generation AI will remove the noise and generate clear audio. If there are gaps in sensor data, the generation AI will fill in the missing parts based on other sensor data. This allows the generation unit to maintain data integrity and quality and improve the reliability of the urban digitalization system. Furthermore, the generation unit regularly updates the generation AI's training data to achieve highly accurate supplementation based on the latest data. This allows the generation unit to always provide high-quality data and improve the overall system performance.

[0075] The rewards department provides rewards based on the data generated by the generation department. For example, the rewards department can provide rewards based on the number of videos that are selected. Specifically, if the collected video data is organized, generated, and ultimately selected, the user who provided the data will be paid a reward. The reward calculation takes into account the quality and importance of the data, as well as the difficulty of collection. For example, high-resolution video data of important locations will be rewarded more highly. The rewards department can also flexibly choose the method of payment for rewards. For example, it can provide rewards according to the user's preference, such as cash, electronic money, or points. The rewards department can also use AI to automate the calculation and payment of rewards. For example, AI can automatically evaluate the quality and importance of the collected data and calculate the optimal reward amount. This ensures that rewards are calculated quickly and accurately, improving user satisfaction. Furthermore, the rewards department manages the reward history, allowing users to check their reward status. This allows users to understand their contribution and reward status, and maintain their motivation. In this way, the rewards department can provide users with appropriate rewards and encourage their cooperation in data collection.

[0076] The Negotiation Department assists in negotiating for permits in locations requiring them, based on the compensation provided by the Compensation Department. For example, the Negotiation Department can assist in negotiating for necessary permits for data collection in commercial and private areas. Specifically, the Negotiation Department negotiates to gain the understanding and cooperation of stakeholders by explaining the purpose and methods of data collection, as well as how the collected data will be used. The Negotiation Department can utilize AI to simulate negotiations and propose optimal negotiation strategies. For example, the AI ​​analyzes past negotiation data to learn successful negotiation patterns and effective negotiation methods, enabling the Negotiation Department to conduct more effective negotiations. Furthermore, the Negotiation Department manages the progress and results of negotiations in real time and takes prompt action as needed. For example, if negotiations are stalled, it can report to higher-level managers and request additional support. In addition, the Negotiation Department can record negotiation results in a database for use in future negotiations. This allows the Negotiation Department to smoothly conduct data collection in locations requiring permits and expand the data collection scope of the urban digitalization system.

[0077] The collection unit can collect video in real time using a glasses-type device. The collection unit can, for example, collect video in real time using a glasses-type device. The collection unit can, for example, collect video while a participant wearing a glasses-type device walks around town. The collection unit can, for example, collect high-resolution video using a glasses-type device. This allows the collection unit to collect video in real time by using a glasses-type device. The glasses-type device includes, for example, camera resolution, communication functions, etc., but is not limited to such examples. Some or all of the processing described above in the collection unit may be performed using, for example, AI, or not using AI. For example, the collection unit can input video data collected by the glasses-type device into a generating AI, and the generating AI can organize and supplement the data.

[0078] The data organization unit can organize and upload data using general-purpose data organization and upload methods. For example, the data organization unit can organize and upload data using general-purpose data organization and upload methods. For example, the data organization unit can organize data using data classification methods and organization algorithms. For example, the data organization unit can upload data using a destination server and upload protocol. This allows the data organization unit to efficiently organize and upload data using general-purpose methods. General-purpose data organization and upload methods include, but are not limited to, the software used and organization algorithms. Some or all of the above-described processes in the data organization unit may be performed using, for example, AI, or not using AI. For example, the data organization unit can input data collected by the data collection unit into a generating AI, and the generating AI can organize and upload the data.

[0079] The generation unit can use the generation AI to fill in any missing parts in the collected video data. For example, if there are missing parts in the collected video data, the generation AI can fill in those parts. The generation unit can use the generation AI to fill in the missing parts and create a seamless virtual city space. For example, if there are missing parts in the collected video data, the generation AI can fill in those parts and provide seamless data. Thus, the generation unit can use the generation AI to fill in the missing parts and provide seamless data. The generation AI includes, but is not limited to, the algorithm used and the type of training data. Some or all of the above processing in the generation unit may be performed using AI or not. For example, the generation unit can input data collected by the collection unit into the generation AI, and the generation AI can fill in the data.

[0080] The rewards unit can provide rewards according to the number of videos selected. The rewards unit can, for example, provide rewards according to the number of videos selected to increase participant motivation. The rewards unit can, for example, provide rewards according to the number of videos selected to increase participant motivation. This allows the rewards unit to increase participant motivation by providing rewards according to the number of videos selected. The number of videos selected includes, but is not limited to, the evaluation criteria for selection and the selection process. Some or all of the above processing in the rewards unit may be performed using, for example, AI, or not using AI. For example, the rewards unit can input the data generated in the generation unit into a generation AI, and the generation AI can provide rewards.

[0081] The Negotiation Department can assist in negotiating to obtain necessary permits for data collection in commercial and private areas. The Negotiation Department can assist in negotiating to obtain necessary permits for data collection in commercial and private areas, for example. The Negotiation Department can assist in negotiating to obtain necessary permits for data collection in commercial and private areas, for example, to ensure that data collection proceeds smoothly. The Negotiation Department can assist in negotiating to obtain necessary permits for data collection in commercial and private areas, for example, to ensure that data collection proceeds smoothly. By assisting in negotiating to obtain necessary permits, the Negotiation Department can ensure that data collection proceeds smoothly. Commercial and private areas include, but are not limited to, commercial facilities and private residences. Some or all of the above processing in the Negotiation Department may be performed using, for example, AI, or not using AI. For example, the Negotiation Department can input the rewards provided by the Reward Department into a Generating AI, and the Generating AI can assist in negotiations.

[0082] The collection unit can estimate the user's emotions and adjust the timing of video collection based on the estimated user emotions. For example, the collection unit can estimate the user's emotions and adjust the timing of video collection based on the estimated user emotions. For example, if the user is relaxed, the collection unit can start video collection at a natural timing. For example, if the user is in a hurry, the collection unit can prioritize video collection of only the important points. For example, if the user is excited, the collection unit can collect video frequently to obtain detailed data. In this way, the collection unit can collect more appropriate video by adjusting the timing of video collection according to the user's emotions. User emotions include, but are not limited to, facial recognition and voice analysis. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) and multimodal generation AI. Some or all of the above processing in the collection unit may be performed using, for example, AI, or not using AI. For example, the data collection unit can input user emotion data into a generating AI, which can then use the generating AI to estimate the emotion.

[0083] The collection unit can analyze the user's past video collection history and select the optimal collection method. For example, the collection unit can analyze the user's past video collection history and select the optimal collection method. For example, the collection unit can analyze the patterns of videos previously collected by the user and perform collection using similar patterns. For example, the collection unit can consider the time periods of videos previously collected by the user and perform collection at the same time periods. For example, the collection unit can refer to the locations of videos previously collected by the user and perform collection at the same locations. In this way, the collection unit can select the optimal collection method by analyzing past collection history. The optimal collection method includes, but is not limited to, the timing of collection and the means of collection. Some or all of the above processing in the collection unit may be performed using, for example, AI, or not using AI. For example, the collection unit can input the user's past video collection history into a generating AI, and the generating AI can select the optimal collection method.

[0084] The collection unit can filter video footage based on the user's current activities and areas of interest. For example, the collection unit can prioritize collecting video footage of tourist attractions if the user is sightseeing, or of commercial areas if the user is shopping, or of commuting if the user is commuting. By filtering based on the user's activities and areas of interest, the collection unit can collect highly relevant video footage. Filtering includes, but is not limited to, filtering conditions and algorithms used. Some or all of the processing described above in the collection unit may be performed using, for example, AI, or not. For example, the collection unit can input the user's activities and areas of interest into a generating AI, which can then perform the filtering.

[0085] The collection unit can estimate the user's emotions and determine the priority of the videos to collect based on the estimated emotions. For example, the collection unit can estimate the user's emotions and determine the priority of the videos to collect based on the estimated emotions. For example, if the user is relaxed, the collection unit can prioritize collecting videos of landscapes and nature. For example, if the user is in a hurry, the collection unit can prioritize collecting videos of important landmarks. For example, if the user is excited, the collection unit can prioritize collecting videos of events and activities. In this way, the collection unit can collect more appropriate videos by prioritizing videos according to the user's emotions. Video prioritization includes, but is not limited to, evaluation criteria and decision-making processes for prioritization. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the collection unit may be performed using, for example, AI, or not using AI. For example, the data collection unit can input user emotion data into a generating AI, which can then use the generating AI to estimate the emotion.

[0086] The collection unit can prioritize the collection of highly relevant videos by considering the user's geographical location information during video collection. For example, if the user is in a tourist area, the collection unit can prioritize the collection of videos of major spots in that tourist area. For example, if the user is in a commercial area, the collection unit can prioritize the collection of videos of shops and facilities in that area. For example, if the user is in a residential area, the collection unit can prioritize the collection of videos of the living environment in that area. In this way, the collection unit can prioritize the collection of highly relevant videos by considering geographical location information. Geographical location information includes, but is not limited to, GPS data and methods for analyzing location information. Some or all of the above processing in the collection unit may be performed using, for example, AI, or without AI. For example, the collection unit can input the user's geographical location information into a generating AI, and the generating AI can prioritize the collection of highly relevant videos.

[0087] The collection unit can analyze the user's social media activity and collect relevant videos when collecting video. For example, the collection unit can analyze the user's social media activity and collect relevant videos when collecting video. For example, the collection unit can prioritize collecting videos of places the user has shared on social media. For example, the collection unit can prioritize collecting videos of events the user has shown interest in on social media. For example, the collection unit can prioritize collecting videos of places related to accounts the user follows on social media. In this way, the collection unit can collect relevant videos by analyzing social media activity. Social media activity includes, but is not limited to, analysis of posted content and analysis of activity history. Some or all of the above processing in the collection unit may be performed using, for example, AI, or not using AI. For example, the collection unit can input the user's social media activity into a generating AI, and the generating AI can collect relevant videos.

[0088] The data sorting unit can estimate the user's emotions and adjust the data sorting method based on the estimated user emotions. For example, the sorting unit can estimate the user's emotions and adjust the data sorting method based on the estimated user emotions. For example, if the user is relaxed, the sorting unit can perform detailed data sorting. For example, if the user is in a hurry, the sorting unit can perform simplified data sorting. For example, if the user is excited, the sorting unit can perform visually appealing data sorting. In this way, the sorting unit can perform more appropriate data sorting by adjusting the data sorting method according to the user's emotions. The data sorting method includes, but is not limited to, sorting algorithms and sorting processes. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) and multimodal generation AI. Some or all of the above processing in the sorting unit may be performed using, for example, AI, or not using AI. For example, the data processing unit can input user emotion data into a generating AI, which can then use the generating AI to estimate the emotion.

[0089] The sorting unit can adjust the level of detail in sorting based on the importance of the collected data during sorting. For example, the sorting unit can sort important data in detail and prioritize its upload. For example, it can sort general data in a simplified manner and postpone it. For example, it can delete unnecessary data to improve sorting efficiency. In this way, the sorting unit can perform efficient data sorting by adjusting the level of detail in sorting based on the importance of the data. Data importance includes, but is not limited to, data value and data usage frequency. Some or all of the above processing in the sorting unit may be performed using, for example, AI, or not using AI. For example, the sorting unit can input the importance of the collected data into a generating AI, and the generating AI can adjust the level of detail in sorting.

[0090] The data sorting unit can apply different sorting algorithms depending on the data category when sorting data. For example, the sorting unit can classify tourist destination data into the tourism category and apply a dedicated sorting algorithm. For example, the sorting unit can classify commercial area data into the commercial category and apply a dedicated sorting algorithm. For example, the sorting unit can classify residential area data into the residential category and apply a dedicated sorting algorithm. This allows the sorting unit to perform efficient data sorting by applying sorting algorithms according to the data category. Data categories include, but are not limited to, data type and data use. Some or all of the above processing in the sorting unit may be performed using, for example, AI, or not using AI. For example, the sorting unit can input data categories into a generating AI, and the generating AI can apply different sorting algorithms.

[0091] The sorting unit can estimate the user's emotions and determine the priority of data sorting based on the estimated user emotions. For example, the sorting unit can estimate the user's emotions and determine the priority of data sorting based on the estimated user emotions. For example, if the user is relaxed, the sorting unit can prioritize detailed data sorting. For example, if the user is in a hurry, the sorting unit can prioritize important data sorting. For example, if the user is excited, the sorting unit can prioritize visually appealing data sorting. In this way, the sorting unit can perform more appropriate data sorting by determining the priority of data sorting according to the user's emotions. The data sorting priority includes, but is not limited to, evaluation criteria and decision processes for priority. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the sorting unit may be performed using AI, for example, or without AI. For example, the data processing unit can input user emotion data into a generating AI, which can then use the generating AI to estimate the emotion.

[0092] The sorting unit can adjust the sorting order based on the data collection date during data sorting. For example, the sorting unit can sort the data based on the data collection date during data sorting. The sorting unit can, for example, prioritize sorting the latest data and upload it quickly. The sorting unit can, for example, sort older data later. The sorting unit can, for example, prioritize sorting data collected during a specific event period. This allows the sorting unit to perform efficient data sorting by adjusting the sorting order based on the data collection date. The data collection date includes, but is not limited to, recording the collection date and time, and methods for analyzing the collection date. Some or all of the above processing in the sorting unit may be performed using, for example, AI, or not using AI. For example, the sorting unit can input the data collection date into a generating AI, and the generating AI can adjust the sorting order.

[0093] The data organization unit can adjust its organization method based on the relationships between data when organizing data. For example, the data organization unit can organize highly relevant data together and upload them efficiently. For example, the data organization unit can organize less relevant data individually and postpone processing it. For example, the data organization unit can analyze the relationships between data and apply the optimal organization method. This allows the data organization unit to perform efficient data organization by adjusting its organization method based on the relationships between data. The relationships between data include, but are not limited to, data correlations and methods for analyzing relationships. Some or all of the above processing in the data organization unit may be performed using, for example, AI, or not using AI. For example, the data organization unit can input the relationships between data into a generating AI, and the generating AI can adjust the organization method.

[0094] The generation unit can estimate the user's emotions and adjust the way the generated data is represented based on the estimated user emotions. For example, the generation unit can estimate the user's emotions and adjust the way the generated data is represented based on the estimated user emotions. For example, if the user is relaxed, the generation unit can generate data in a calm way. For example, if the user is in a hurry, the generation unit can generate data in a concise way. For example, if the user is excited, the generation unit can generate data in a visually stimulating way. In this way, the generation unit can generate more appropriate data by adjusting the way the data is represented according to the user's emotions. The way the data is represented includes, but is not limited to, the format of representation and the level of detail of representation. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the generation unit may be performed using, for example, AI, or not using AI. For example, the generation unit can input user emotion data into the generation AI, which can then estimate the emotion.

[0095] The generation unit can adjust the level of detail of the generated data based on the importance of the missing data during generation. For example, the generation unit can adjust the level of detail of the generated data based on the importance of the missing data during generation. For example, the generation unit can generate important missing data in detail to provide seamless data. For example, the generation unit can generate general missing data in a simplified manner. For example, the generation unit can improve efficiency by not generating unnecessary missing data. In this way, the generation unit can perform efficient data generation by adjusting the level of detail of the generated data based on the importance of the missing data. The importance of the missing data includes, but is not limited to, the degree of impact of the missing data and the criteria for evaluating importance. Some or all of the above processing in the generation unit may be performed using, for example, AI, or not using AI. For example, the generation unit can input the importance of the missing data into the generation AI, and the generation AI can adjust the level of detail of the generated data.

[0096] The generation unit can apply different generation algorithms depending on the data category during generation. For example, the generation unit can classify tourist destination data into the tourism category and apply a dedicated generation algorithm. For example, the generation unit can classify commercial area data into the commercial category and apply a dedicated generation algorithm. For example, the generation unit can classify residential area data into the residential category and apply a dedicated generation algorithm. This allows the generation unit to efficiently generate data by applying a generation algorithm according to the data category. The generation algorithm includes, but is not limited to, the type of algorithm used and the method of implementing the algorithm. Some or all of the above-described processes in the generation unit may be performed using, for example, AI, or without AI. For example, the generation unit can input the data category into a generation AI, and the generation AI can apply different generation algorithms.

[0097] The generation unit can estimate the user's emotions and determine the priority of the data to be generated based on the estimated user emotions. For example, the generation unit can estimate the user's emotions and determine the priority of the data to be generated based on the estimated user emotions. For example, if the user is relaxed, the generation unit can prioritize the generation of detailed data. For example, if the user is in a hurry, the generation unit can prioritize the generation of important data. For example, if the user is excited, the generation unit can prioritize the generation of visually appealing data. In this way, the generation unit can generate more appropriate data by determining the priority of data according to the user's emotions. Data prioritization includes, but is not limited to, evaluation criteria and decision processes for prioritization. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the generation unit may be performed using, for example, AI, or not using AI. For example, the generation unit can input user emotion data into the generation AI, which can then estimate the emotion.

[0098] The generation unit can adjust the generation order based on the data collection timing during generation. For example, the generation unit can adjust the generation order based on the data collection timing during generation. The generation unit can, for example, prioritize the generation of the latest data and provide it quickly. The generation unit can, for example, postpone the generation of older data. The generation unit can, for example, prioritize the generation of data collected during a specific event period. This allows the generation unit to perform efficient data generation by adjusting the generation order based on the data collection timing. The data collection timing includes, but is not limited to, recording the date and time of collection and methods for analyzing the collection timing. Some or all of the above-described processes in the generation unit may be performed using, for example, AI, or not using AI. For example, the generation unit can input the data collection timing into a generation AI, and the generation AI can adjust the generation order.

[0099] The generation unit can adjust its generation method based on the relevance of the data during generation. For example, the generation unit can adjust its generation method based on the relevance of the data during generation. For example, the generation unit can generate highly relevant data together and provide it efficiently. For example, the generation unit can generate less relevant data individually and postpone it. For example, the generation unit can analyze the relevance of the data and apply the optimal generation method. In this way, the generation unit can perform efficient data generation by adjusting its generation method based on the relevance of the data. The relevance of the data includes, but is not limited to, data correlation and methods for analyzing relevance. Some or all of the above-described processes in the generation unit may be performed using, for example, AI, or not using AI. For example, the generation unit can input the relevance of the data into a generation AI, and the generation AI can adjust the generation method.

[0100] The reward unit can estimate the user's emotions and adjust the method of providing rewards based on the estimated emotions. For example, the reward unit can estimate the user's emotions and adjust the method of providing rewards based on the estimated emotions. For example, if the user is relaxed, the reward unit can provide rewards in stages. For example, if the user is in a hurry, the reward unit can provide rewards all at once. For example, if the user is excited, the reward unit can provide rewards in a visually appealing way. In this way, the reward unit can provide more appropriate rewards by adjusting the method of providing rewards according to the user's emotions. The method of providing rewards includes, but is not limited to, the type of reward and the timing of its provision. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the reward unit may be performed using, for example, AI, or not using AI. For example, the reward unit can input user emotion data into a generating AI, which can then use the generating AI to estimate the emotion.

[0101] The rewards unit can adjust the level of detail of rewards based on the importance of the collected footage when providing rewards. For example, the rewards unit can adjust the level of detail of rewards based on the importance of the collected footage when providing rewards. For example, the rewards unit can provide high rewards for important footage. For example, the rewards unit can provide standard rewards for general footage. For example, the rewards unit can not provide rewards for unnecessary footage. This allows the rewards unit to provide rewards efficiently by adjusting the level of detail of rewards based on the importance of the collected footage. The importance of the collected footage includes, but is not limited to, the value of the footage or the frequency of use of the footage. Some or all of the processing described above in the rewards unit may be performed using, for example, AI, or not using AI. For example, the rewards unit can input the importance of the collected footage into a generating AI, and the generating AI can adjust the level of detail of the rewards.

[0102] The reward unit can apply different reward algorithms depending on the video category when providing rewards. For example, the reward unit can apply a different reward algorithm depending on the video category when providing rewards. For example, the reward unit can apply a reward algorithm for the tourism category to videos of tourist destinations. For example, the reward unit can apply a reward algorithm for the commercial category to videos of commercial areas. For example, the reward unit can apply a reward algorithm for the residential category to videos of residential areas. This allows the reward unit to provide rewards efficiently by applying a reward algorithm according to the video category. The reward algorithm includes, but is not limited to, the type of algorithm used and the method of implementing the algorithm. Some or all of the above processing in the reward unit may be performed using, for example, AI, or not using AI. For example, the reward unit can input the video category into a generating AI, and the generating AI can apply different reward algorithms.

[0103] The reward unit can estimate the user's emotions and determine the priority of rewards based on the estimated emotions. For example, the reward unit can estimate the user's emotions and determine the priority of rewards based on the estimated emotions. For example, if the user is relaxed, the reward unit can prioritize gradual rewards. For example, if the user is in a hurry, the reward unit can prioritize a lump-sum reward. For example, if the user is excited, the reward unit can prioritize visually appealing rewards. In this way, the reward unit can provide more appropriate rewards by determining the priority of rewards according to the user's emotions. The priority of rewards includes, but is not limited to, evaluation criteria and decision-making processes for prioritizing. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reward unit may be performed using, for example, AI, or not using AI. For example, the reward unit can input user emotion data into a generating AI, which can then use the generating AI to estimate the emotion.

[0104] The rewards unit can adjust the order of rewards based on the video collection date when providing rewards. For example, the rewards unit can adjust the order of rewards based on the video collection date when providing rewards. For example, the rewards unit can prioritize rewards for the most recent videos. For example, the rewards unit can postpone rewards for older videos. For example, the rewards unit can prioritize rewards for videos collected during a specific event period. This allows the rewards unit to provide rewards efficiently by adjusting the order of rewards based on the video collection date. The video collection date includes, but is not limited to, recording the collection date and time, and methods for analyzing the collection date. Some or all of the above processing in the rewards unit may be performed using, for example, AI, or not using AI. For example, the rewards unit can input the video collection date into a generating AI, and the generating AI can adjust the order of rewards.

[0105] The reward unit can adjust the reward method based on the relevance of the videos when providing rewards. For example, the reward unit can adjust the reward method based on the relevance of the videos when providing rewards. For example, the reward unit can provide high rewards for highly relevant videos. For example, the reward unit can provide standard rewards for less relevant videos. For example, the reward unit can not provide rewards for unnecessary videos. In this way, the reward unit can provide rewards efficiently by adjusting the reward method based on the relevance of the videos. Video relevance includes, but is not limited to, video correlations and methods for analyzing relevance. Some or all of the above processing in the reward unit may be performed using, for example, AI, or not using AI. For example, the reward unit can input the relevance of the videos into a generating AI, and the generating AI can adjust the reward method.

[0106] The negotiation unit can estimate the user's emotions and adjust its negotiation methods based on the estimated emotions. For example, the negotiation unit can estimate the user's emotions and adjust its negotiation methods based on the estimated emotions. For example, if the user is relaxed, the negotiation unit can adopt a gentle negotiation method. For example, if the user is in a hurry, the negotiation unit can adopt a rapid negotiation method. For example, if the user is excited, the negotiation unit can adopt an aggressive negotiation method. In this way, the negotiation unit can conduct more appropriate negotiations by adjusting its negotiation methods according to the user's emotions. Negotiation methods include, but are not limited to, negotiation means and negotiation processes. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the negotiation unit may be performed using AI, for example, or without AI. For example, the negotiation department can input user emotion data into a generative AI, which can then use the AI ​​to estimate emotions.

[0107] The negotiating unit can adjust the level of detail in negotiations based on the importance of the collected data. For example, the negotiating unit can adjust the level of detail in negotiations based on the importance of the collected data. For example, the negotiating unit can conduct detailed negotiations for important data. For example, the negotiating unit can conduct simplified negotiations for general data. For example, the negotiating unit can not negotiate for unnecessary data. This allows the negotiating unit to conduct efficient negotiations by adjusting the level of detail in negotiations based on the importance of the collected data. The importance of the collected data includes, but is not limited to, data value and data usage frequency. Some or all of the above processing in the negotiating unit may be performed using, for example, AI, or not using AI. For example, the negotiating unit can input the importance of the collected data into a generating AI, and the generating AI can adjust the level of detail in negotiations.

[0108] The negotiation unit can apply different negotiation algorithms depending on the data category during negotiations. For example, the negotiation unit can apply a negotiation algorithm for the tourism category to tourist destination data. For example, the negotiation unit can apply a negotiation algorithm for the commercial category to commercial area data. For example, the negotiation unit can apply a negotiation algorithm for the residential area data. This allows the negotiation unit to conduct efficient negotiations by applying a negotiation algorithm according to the data category. Negotiation algorithms include, but are not limited to, the type of algorithm used and the method of implementing the algorithm. Some or all of the above-described processes in the negotiation unit may be performed using, for example, AI, or not using AI. For example, the negotiation unit can input the data category into a generating AI, and the generating AI can apply different negotiation algorithms.

[0109] The negotiation unit can estimate the user's emotions and determine negotiation priorities based on the estimated emotions. For example, the negotiation unit can estimate the user's emotions and determine negotiation priorities based on the estimated emotions. For example, if the user is relaxed, the negotiation unit can prioritize calm negotiations. For example, if the user is in a hurry, the negotiation unit can prioritize quick negotiations. For example, if the user is excited, the negotiation unit can prioritize aggressive negotiations. This allows the negotiation unit to conduct more appropriate negotiations by determining negotiation priorities according to the user's emotions. Negotiation priorities include, but are not limited to, evaluation criteria and decision-making processes. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the negotiation unit may be performed using AI or not using AI. For example, the negotiation unit can input user emotion data into a generative AI, and the generative AI can estimate the emotions.

[0110] The negotiating unit can adjust the order of negotiations based on the timing of data collection during negotiations. For example, the negotiating unit can prioritize negotiations on the most recent data. For example, the negotiating unit can postpone negotiations on older data. For example, the negotiating unit can prioritize negotiations on data collected during a specific event period. This allows the negotiating unit to conduct efficient negotiations by adjusting the order of negotiations based on the timing of data collection. The timing of data collection includes, but is not limited to, recording the date and time of collection and methods for analyzing the timing of collection. Some or all of the above processing in the negotiating unit may be performed using, for example, AI, or not using AI. For example, the negotiating unit can input the timing of data collection into a generating AI, and the generating AI can adjust the order of negotiations.

[0111] The negotiating unit can adjust its negotiation methods based on the relevance of the data during negotiations. For example, the negotiating unit can adjust its negotiation methods based on the relevance of the data during negotiations. For example, the negotiating unit can conduct detailed negotiations for highly relevant data. For example, the negotiating unit can conduct simplified negotiations for less relevant data. For example, the negotiating unit can refrain from negotiating for unnecessary data. This allows the negotiating unit to conduct efficient negotiations by adjusting its negotiation methods based on the relevance of the data. The relevance of the data includes, but is not limited to, data correlations and methods for analyzing relevances. Some or all of the above processing in the negotiating unit may be performed using, for example, AI, or not using AI. For example, the negotiating unit can input the relevance of the data into a generating AI, and the generating AI can adjust the negotiation method.

[0112] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0113] The data collection unit can estimate the user's emotions and adjust the timing of video collection based on the estimated emotions. For example, if the user is relaxed, video collection can start at a natural timing. If the user is in a hurry, video collection can be prioritized to capture only the important points. If the user is excited, video collection can be performed frequently to obtain detailed data. In this way, the data collection unit can collect more appropriate video by adjusting the timing of video collection according to the user's emotions. User emotions include, but are not limited to, facial recognition and voice analysis. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) and multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input user emotion data into a generative AI, and the generative AI can perform emotion estimation.

[0114] The data sorting unit can adjust the level of detail in the sorting process based on the importance of the collected data. For example, important data can be sorted in detail and uploaded preferentially. General data can be sorted simply and postponed. Unnecessary data can be deleted to improve the efficiency of the sorting process. In this way, the data sorting unit can perform efficient data sorting by adjusting the level of detail in the sorting process based on the importance of the data. Data importance includes, but is not limited to, data value and data usage frequency. Some or all of the above processing in the data sorting unit may be performed using, for example, AI, or not using AI. For example, the data sorting unit can input the importance of the collected data into a generating AI, and the generating AI can adjust the level of detail in the sorting process.

[0115] The generation unit can adjust the level of detail of the generated data based on the importance of the missing data. For example, important missing data can be generated in detail to provide seamless data. General missing data can be generated in a simplified manner. Unnecessary missing data can be omitted to improve efficiency. In this way, the generation unit can perform efficient data generation by adjusting the level of detail of the generated data based on the importance of the missing data. The importance of the missing data includes, but is not limited to, the degree of impact of the missing data and the criteria for evaluating importance. Some or all of the above processing in the generation unit may be performed using AI, for example, or not using AI. For example, the generation unit can input the importance of the missing data into the generation AI, and the generation AI can adjust the level of detail of the generated data.

[0116] The reward unit can estimate the user's emotions and adjust the method of reward delivery based on the estimated emotions. For example, if the user is relaxed, rewards can be provided in stages. If the user is in a hurry, rewards can be provided all at once. If the user is excited, rewards can be provided in a visually appealing way. In this way, the reward unit can provide more appropriate rewards by adjusting the method of reward delivery according to the user's emotions. The method of reward delivery includes, but is not limited to, the type of reward and the timing of its provision. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the reward unit may be performed using AI, for example, or without AI. For example, the reward unit can input user emotion data into a generative AI, and the generative AI can perform emotion estimation.

[0117] The negotiation unit can estimate the user's emotions and adjust its negotiation methods based on those emotions. For example, if the user is relaxed, a gentle negotiation method can be adopted. If the user is in a hurry, a swift negotiation method can be adopted. If the user is excited, an aggressive negotiation method can be adopted. This allows the negotiation unit to conduct more appropriate negotiations by adjusting its methods according to the user's emotions. Negotiation methods include, but are not limited to, negotiation means and negotiation processes. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the negotiation unit may be performed using AI, or not using AI. For example, the negotiation unit can input user emotion data into a generative AI, which can then estimate the emotion.

[0118] The collection unit can analyze the user's past video collection history and select the optimal collection method. For example, it can analyze patterns in videos previously collected by the user and collect videos using similar patterns. It can consider the time periods of videos previously collected by the user and collect videos at the same time periods. It can refer to the locations of videos previously collected by the user and collect videos at the same locations. In this way, the collection unit can select the optimal collection method by analyzing past collection history. The optimal collection method includes, but is not limited to, the timing of collection and the means of collection. Some or all of the above processing in the collection unit may be performed using AI, for example, or without AI. For example, the collection unit can input the user's past video collection history into a generating AI, and the generating AI can select the optimal collection method.

[0119] The collection unit can filter video footage based on the user's current activities and areas of interest. For example, if the user is sightseeing, it can prioritize collecting footage of tourist attractions. If the user is shopping, it can prioritize collecting footage of commercial areas. If the user is commuting, it can prioritize collecting footage of their commute route. This allows the collection unit to collect highly relevant footage by filtering based on the user's activities and areas of interest. Filtering includes, but is not limited to, filtering conditions and the algorithms used. Some or all of the processing described above in the collection unit may be performed using AI, for example, or without AI. For example, the collection unit can input the user's activities and areas of interest into a generating AI, which can then perform the filtering.

[0120] The data sorting unit can apply different sorting algorithms depending on the data category during data sorting. For example, data for tourist destinations can be classified into the tourism category and a dedicated sorting algorithm can be applied. Data for commercial areas can be classified into the commercial category and a dedicated sorting algorithm can be applied. Data for residential areas can be classified into the residential category and a dedicated sorting algorithm can be applied. This allows the data sorting unit to perform efficient data sorting by applying sorting algorithms according to the data category. Data categories include, but are not limited to, data type and data use. Some or all of the above processing in the data sorting unit may be performed using, for example, AI, or not using AI. For example, the data sorting unit can input data categories into a generating AI, and the generating AI can apply different sorting algorithms.

[0121] The generation unit can adjust the generation order based on the data collection timing during generation. For example, it can prioritize generating the latest data and provide it quickly. Older data can be generated later. Data collected during a specific event period can be prioritized. This allows the generation unit to efficiently generate data by adjusting the generation order based on the data collection timing. The data collection timing includes, but is not limited to, recording the collection date and time, and methods for analyzing the collection timing. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input the data collection timing into a generation AI, and the generation AI can adjust the generation order.

[0122] The negotiation department can adjust its negotiation methods based on the relevance of the data during negotiations. For example, it can conduct detailed negotiations for highly relevant data, simplified negotiations for less relevant data, and no negotiations for unnecessary data. This allows the negotiation department to conduct efficient negotiations by adjusting its methods based on the relevance of the data. Data relevance includes, but is not limited to, data correlations and methods of analyzing relevance. Some or all of the above-described processes in the negotiation department may be performed using AI, for example, or not using AI. For example, the negotiation department can input data relevances into a generative AI, which can then adjust the negotiation method.

[0123] The following briefly describes the processing flow for example form 2.

[0124] Step 1: The data collection unit collects data. For example, video can be collected in real time using a glasses-type device. Step 2: The organization unit organizes and uploads the data collected by the collection unit. For example, the data can be organized and uploaded using a general-purpose data organization and upload method. Step 3: The generation unit fills in any missing parts based on the data organized and uploaded by the organization unit. For example, if there are gaps in the collected video data, the generation AI can fill in those gaps. Step 4: The reward unit provides rewards based on the data generated by the generation unit. For example, rewards can be provided based on the number of videos selected. Step 5: The Negotiation Department assists in negotiating for necessary permits based on the compensation provided by the Compensation Department. For example, they can assist in negotiating for necessary permits for data collection in commercial or private areas.

[0125] 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.

[0126] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0127] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0128] Each of the multiple elements described above, including the collection unit, sorting unit, generation unit, reward unit, and negotiation unit, is implemented by, for example, at least one of the smart device 14 and the data processing unit 12. For example, the collection unit is implemented by the smart device 14 and collects video in real time using the camera 42 and communication I / F 44 of the smart device 14. The sorting unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and sorts and uploads the collected data. The generation unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and uses generation AI to supplement any missing parts. The reward unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and provides a reward according to the number of videos used. The negotiation unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and assists in negotiating locations where permission is required. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0129] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0130] 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.

[0131] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0132] 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.

[0133] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0134] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0135] 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.

[0136] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0137] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0138] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0139] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0140] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0141] 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.

[0142] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0143] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0144] Each of the multiple elements described above, including the collection unit, sorting unit, generation unit, reward unit, and negotiation unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the collection unit is implemented by the smart glasses 214 and collects video in real time using the smart glasses 214's camera 42 and communication I / F 44. The sorting unit is implemented by the specific processing unit 290 of the data processing unit 12 and sorts and uploads the collected data. The generation unit is implemented by the specific processing unit 290 of the data processing unit 12 and uses generation AI to supplement any missing parts. The reward unit is implemented by the specific processing unit 290 of the data processing unit 12 and provides a reward according to the number of videos used. The negotiation unit is implemented by the specific processing unit 290 of the data processing unit 12 and assists in negotiating locations where permission is required. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0145] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0146] 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.

[0147] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0148] 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.

[0149] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0150] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0151] 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.

[0152] 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.

[0153] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0154] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0155] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0156] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0157] 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.

[0158] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0159] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0160] Each of the multiple elements described above, including the collection unit, sorting unit, generation unit, reward unit, and negotiation unit, is implemented by, for example, at least one of the headset terminal 314 and the data processing unit 12. For example, the collection unit is implemented by the headset terminal 314 and collects video in real time using the camera 42 and communication I / F 44 of the headset terminal 314. The sorting unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and sorts and uploads the collected data. The generation unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and uses generation AI to supplement any missing parts. The reward unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and provides a reward according to the number of videos used. The negotiation unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and assists in negotiating locations where permission is required. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0161] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0162] 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.

[0163] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0164] 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.

[0165] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0166] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0167] 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.

[0168] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0169] 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.

[0170] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

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

[0172] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

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

[0174] The specific processing unit 290 transmits the result of the specific processing to the 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.

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

[0176] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0177] Each of the multiple elements described above, including the collection unit, sorting unit, generation unit, reward unit, and negotiation unit, is implemented by, for example, at least one of the robot 414 and the data processing unit 12. For example, the collection unit is implemented by the robot 414 and collects video in real time using the robot 414's camera 42 and communication I / F 44. The sorting unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and sorts and uploads the collected data. The generation unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and uses generation AI to supplement any missing parts. The reward unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and provides a reward according to the number of videos used. The negotiation unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and assists in negotiating locations where permission is required. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0178] 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.

[0179] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0180] 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.

[0181] 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.

[0182] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0183] 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."

[0184] 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.

[0185] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0186] 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.

[0187] 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.

[0188] 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.

[0189] 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.

[0190] 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.

[0191] 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.

[0192] 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.

[0193] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0194] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0195] 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.

[0196] (Note 1) A data collection unit that collects data, A sorting unit sorts and uploads the data collected by the aforementioned collection unit, A generation unit that supplements the missing parts based on the data organized and uploaded by the aforementioned organization unit, A reward unit provides a reward based on the data generated by the generation unit, The system comprises a negotiation department that assists in negotiating locations where permits are required based on the remuneration provided by the aforementioned remuneration department. A system characterized by the following features. (Note 2) The aforementioned collection unit is Collect video in real time using glasses-type devices. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned editing unit, Organize and upload data using general-purpose data organization and upload methods. The system described in Appendix 1, characterized by the features described herein. (Note 4) The generating unit is If there are gaps in the collected video data, the generating AI will fill in those gaps. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned compensation unit is, Compensation will be provided based on the number of videos selected. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned negotiating body said, We assist in negotiating necessary permits for data collection in commercial and private areas. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned collection unit is The system estimates the user's emotions and adjusts the timing of video collection based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned collection unit is The system analyzes the user's past video collection history and selects the optimal collection method. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned collection unit is When collecting video footage, filtering is performed based on the user's current activities and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned collection unit is It estimates the user's emotions and determines the priority of the videos to collect based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned collection unit is When collecting video footage, the system prioritizes collecting highly relevant footage by considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned collection unit is When collecting video footage, the system analyzes users' social media activity and collects relevant videos. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned editing unit, We estimate user emotions and adjust the data processing method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned editing unit, When organizing data, adjust the level of detail based on the importance of the collected data. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned editing unit, When organizing data, different organization algorithms are applied depending on the data category. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned editing unit, We estimate user emotions and determine data organization priorities based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned editing unit, When organizing data, adjust the order of organization based on when the data was collected. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned editing unit, When organizing data, adjust the organization method based on the relationships between the data. The system described in Appendix 1, characterized by the features described herein. (Note 19) The generating unit is It estimates user emotions and adjusts how the generated data is represented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The generating unit is During generation, the level of detail of the generation is adjusted based on the importance of the missing data. The system described in Appendix 1, characterized by the features described herein. (Note 21) The generating unit is During generation, different generation algorithms are applied depending on the data category. The system described in Appendix 1, characterized by the features described herein. (Note 22) The generating unit is It estimates the user's emotions and determines the priority of the data to generate based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The generating unit is During generation, the generation order is adjusted based on when the data was collected. The system described in Appendix 1, characterized by the features described herein. (Note 24) The generating unit is During generation, the generation method is adjusted based on the relevance of the data. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned compensation unit is, The system estimates the user's emotions and adjusts the reward system based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned compensation unit is, When providing rewards, the level of detail of the rewards will be adjusted based on the importance of the collected footage. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned compensation unit is, When providing rewards, different reward algorithms are applied depending on the video category. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned compensation unit is, The system estimates the user's emotions and prioritizes rewards based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned compensation unit is, When providing rewards, the order of rewards will be adjusted based on when the video footage was collected. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned compensation unit is, When providing compensation, we adjust the compensation method based on the relevance of the video. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned negotiating body said, It estimates the user's emotions and adjusts the negotiation method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 32) The aforementioned negotiating body said, During negotiations, adjust the level of detail based on the importance of the collected data. The system described in Appendix 1, characterized by the features described herein. (Note 33) The aforementioned negotiating body said, During negotiations, different negotiation algorithms are applied depending on the data category. The system described in Appendix 1, characterized by the features described herein. (Note 34) The aforementioned negotiating body said, The system estimates the user's emotions and determines negotiation priorities based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 35) The aforementioned negotiating body said, During negotiations, adjust the order of negotiations based on when the data was collected. The system described in Appendix 1, characterized by the features described herein. (Note 36) The aforementioned negotiating body said, During negotiations, adjust your negotiation approach based on the relevance of the data. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

[0197] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A data collection unit that collects data, A sorting unit sorts and uploads the data collected by the aforementioned collection unit, A generation unit that supplements the missing parts based on the data organized and uploaded by the aforementioned organization unit, A reward unit provides a reward based on the data generated by the generation unit, The system comprises a negotiation department that assists in negotiating locations where permits are required based on the remuneration provided by the aforementioned remuneration department. A system characterized by the following features.

2. The aforementioned collection unit is Collect video in real time using glasses-type devices. The system according to feature 1.

3. The aforementioned editing unit, Organize and upload data using general-purpose data organization and upload methods. The system according to feature 1.

4. The generating unit is If there are gaps in the collected video data, the generating AI will fill in those gaps. The system according to feature 1.

5. The aforementioned compensation unit is, Compensation will be provided based on the number of videos selected. The system according to feature 1.

6. The aforementioned negotiating body said, We assist in negotiating necessary permits for data collection in commercial and private areas. The system according to feature 1.

7. The aforementioned collection unit is The system estimates the user's emotions and adjusts the timing of video collection based on those estimated emotions. The system according to feature 1.

8. The aforementioned collection unit is The system analyzes the user's past video collection history and selects the optimal collection method. The system according to feature 1.

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