system

Abstract

The system according to this embodiment aims to efficiently provide service comparisons and the latest information in the image generation AI market, improve existing technologies, and invest in promising companies. [Solution] The system according to the embodiment comprises a comparison unit, a collection unit, a provision unit, an improvement unit, and an investment unit. The comparison unit provides service comparison information. The collection unit collects the latest information. The provision unit provides the information collected by the collection unit. The improvement unit improves existing technologies. The investment unit invests in promising companies.

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, and includes 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 chaos of enterprises and services, information update speed, and technical hurdles in the image generation AI market, and there is a lack of means to efficiently solve these problems.

[0005] The system according to the embodiment aims to efficiently perform service comparison and provide the latest information in the image generation AI market, improve existing technologies, and invest in promising future enterprises.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a comparison unit, a collection unit, a provision unit, an improvement unit, and an investment unit. The comparison unit provides service comparison information. The collection unit collects the latest information. The provision unit provides the information collected by the collection unit. The improvement unit improves existing technologies. The investment unit invests in promising companies. [Effects of the Invention]

[0007] The system according to this embodiment can efficiently provide service comparisons and the latest information in the image generation AI market, improve existing technologies, and invest in promising companies. [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, etc. The communication I / F manages communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The image generation AI market problem-solving platform according to an embodiment of the present invention is a system that provides service comparison information, collects the latest information, improves existing technologies, and invests in promising companies. This system aims to solve problems such as the proliferation of companies and services, the speed of information updates, and technical hurdles. For example, the platform that provides service comparison information provides comparative reviews and rankings of image generation AI services and provides the latest technology trends and market information with rapid updates. When users choose a specific image generation AI service, they can compare the features and evaluations of each service. In addition, by providing the latest technology information, it ensures that users are always up-to-date. Next, it improves and applies existing technologies. A team of experts is formed to investigate and research existing image generation technologies and develop further improvements and application technologies. For example, new algorithms are applied to publicly available technologies to improve performance. In addition, technical hurdles are lowered by providing customized solutions for companies. Furthermore, it invests in promising companies. Investments are made in companies that are judged to be "promising" through research. For example, funding, technical support, and business consulting are provided to startups that are developing new technologies and services in the image generation AI field. This will accelerate the growth of businesses and drive the overall growth of the image generation AI market. This platform will address the challenges of the image generation AI market, enabling businesses and researchers to select the optimal technologies and services based on reliable information. It also aims to promote technological advancement and application, building a sustainable ecosystem. Thus, the image generation AI market challenge-solving platform will allow businesses and researchers to select the optimal technologies and services based on reliable information.

[0029] The image generation AI market problem-solving platform according to this embodiment comprises a comparison unit, a collection unit, a provision unit, an improvement unit, and an investment unit. The comparison unit provides service comparison information. For example, the comparison unit can compare the features and evaluations of image generation AI services. The comparison unit allows users to compare the features and evaluations of each service when selecting a specific image generation AI service. The collection unit collects the latest information. For example, the collection unit quickly updates and provides the latest technology trends and market information. The collection unit ensures that users are always up-to-date. The provision unit provides the information collected by the collection unit. For example, the provision unit provides service comparison reviews and rankings. The provision unit allows users to compare the features and evaluations of each service when selecting a specific image generation AI service. The improvement unit improves existing technologies. For example, the improvement unit investigates and studies existing image generation technologies and develops further improvements and application technologies. The improvement unit applies new algorithms to published technologies to improve performance. The investment unit invests in promising companies. The investment department provides funding, technical support, and business consulting to startups developing new technologies and services in fields such as AI image generation. This enables the AI ​​image generation market's problem-solving platform to allow companies and researchers to select the optimal technologies and services based on reliable information. Some or all of the processes described above in the investment department may be performed using AI, or not. For example, the investment department could input evaluations of promising companies into AI and have the AI ​​prioritize investments.

[0030] The comparison section provides service comparison information. Specifically, it allows for detailed comparison of the features and evaluations of image generation AI services. For example, it compares a wide range of factors such as generation speed, generation quality, ease of use of the user interface, pricing, and support system for each service. This allows users to select the service that best suits their needs. The comparison section provides an interface for users to compare the features and evaluations of each service when choosing a specific image generation AI service. This interface is designed so that users can easily view detailed information about each service and compare them. For example, users can view the evaluations and reviews of a particular service and compare them with other services. The comparison section also collects user feedback and continuously updates service evaluations. This ensures that the comparison section always provides the latest information and helps users make the best choice. Furthermore, the comparison section also has a function that automatically analyzes the evaluation of each service using AI and recommends the best service to the user. For example, it can implement an algorithm in which the AI ​​recommends the best service based on the user's past selection history and evaluations. This allows the comparison section to provide personalized service comparison information tailored to the user's needs.

[0031] The data collection unit gathers the latest information. Specifically, it quickly updates and provides the latest technology trends and market information. The data collection unit gathers information from publicly available information on the internet, specialized databases, and industry news sites. For example, it collects the latest research papers and technology reports, industry event information, and company press releases and provides them to users. The data collection unit can use AI to collect and analyze information. For example, it can automatically collect information from the internet using web scraping technology and analyze the collected information using natural language processing technology. This allows the data collection unit to quickly extract important information from a vast amount of data and provide it to users. Furthermore, the data collection unit can filter information based on user interests and provide personalized information. For example, if a user is interested in a particular technology or company, it will prioritize providing relevant information based on that interest. This allows the data collection unit to ensure that users are always up-to-date and support quick and accurate decision-making.

[0032] The service provider provides information collected by the data collection department. Specifically, it provides service comparison reviews and rankings. The service provider provides information to help users compare the features and ratings of each service when choosing a specific image generation AI service. For example, it creates evaluations and rankings of each service based on the latest technology trends and market information collected by the data collection department and provides them to users. The service provider provides a user-friendly interface so that users can easily access the information. For example, it allows users to view service comparison reviews and rankings through websites and mobile apps. The service provider also collects user feedback and continuously improves the accuracy and reliability of the information it provides. This enables the service provider to help users choose the optimal service. Furthermore, the service provider can analyze the collected information using AI and provide personalized information to users. For example, it can implement an algorithm where AI recommends the optimal service based on the user's past selection history and evaluations. This enables the service provider to provide information tailored to the user's needs and help users make the best choices.

[0033] The Improvement Department improves existing technologies. Specifically, it investigates and researches existing image generation technologies and develops further improvements and applied technologies. The Improvement Department applies new algorithms to publicly available technologies to improve performance. For example, it incorporates the latest research results into existing image generation AI algorithms to improve generation quality and speed. The Improvement Department can also improve technologies based on user feedback. For example, if users are dissatisfied with a particular function or performance, it will use that feedback to make improvements and develop technologies that meet user needs. Furthermore, the Improvement Department can use AI to improve technologies. For example, by entrusting parameter tuning of generation AI and model optimization to AI, it can improve technologies efficiently and effectively. This allows the Improvement Department to constantly incorporate the latest technologies and improve performance. Furthermore, the Improvement Department can improve technologies in collaboration with other departments. For example, the Improvement Department improves technologies based on the latest technology trends and market information collected by the Data Collection Department. Also, the Improvement Department improves technologies based on user feedback collected by the Data Provision Department. This allows the Improvement Department to improve technologies efficiently and effectively in collaboration with other departments, thereby improving the overall system performance.

[0034] The Investment Department invests in promising companies. Specifically, it provides funding, technical support, and business consulting to startups developing new technologies and services in the field of AI image generation. For example, the Investment Department can input evaluations of promising companies into AI and have the AI ​​prioritize investments. The AI ​​analyzes a wide range of factors, such as a company's technological capabilities, business model, and market potential, to identify the optimal investment targets. This allows the Investment Department to invest efficiently and effectively. Furthermore, the Investment Department provides technical support and business consulting to portfolio companies to support their growth. For example, if a portfolio company faces technical challenges, the Investment Department provides specialized knowledge and resources to help solve those challenges. It also supports the improvement of the company's business model and the formulation of market strategies through business consulting. In this way, the Investment Department can promote the growth of portfolio companies and contribute to the development of the overall AI image generation market. In addition, the Investment Department continuously monitors the performance of portfolio companies and provides additional support as needed. For example, it provides additional funding or technical support depending on the company's growth status and market fluctuations. This will allow the investment department to fully support the success of its portfolio companies and contribute to solving the challenges in the image generation AI market.

[0035] The data collection unit can collect the latest technology trends and market information. For example, the data collection unit can collect the latest technology trends. For example, the data collection unit can collect technology trends such as AI, blockchain, and IoT. For example, the data collection unit can collect market information. For example, the data collection unit can collect market information such as competitive analysis, consumer trends, and sales data. This allows for the rapid collection and provision of the latest technology trends and market information. Some or all of the above-described processes in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the latest technology trends and market information into AI and have the AI ​​perform the information collection.

[0036] The service provider can provide comparative reviews and rankings of services based on the collected information. For example, the service provider can provide comparative reviews of services based on the collected information. For example, the service provider can provide comparative reviews of services based on criteria such as evaluation items, scoring methods, and the reliability of the reviews. For example, the service provider can provide rankings of services based on the collected information. For example, the service provider can provide rankings of services based on criteria such as evaluation standards, scoring methods, and the frequency of ranking updates. This allows users to compare the features and evaluations of each service when choosing a specific image generation AI service. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the collected information into an AI and have the AI ​​perform the provision of comparative reviews and rankings of services.

[0037] The Improvement Unit can investigate and research existing image generation technologies and develop further improvements and applied technologies. For example, the Improvement Unit can investigate and research existing image generation technologies. For example, the Improvement Unit can investigate and research image generation technologies such as GAN, VAE, and deep learning. For example, the Improvement Unit can develop further improvements and applied technologies. For example, the Improvement Unit can develop applied technologies such as medical image analysis, art generation, and autonomous driving. This lowers technical hurdles by improving existing technologies and developing applied technologies. Some or all of the above-mentioned processes in the Improvement Unit may be performed using AI, for example, or without AI. For example, the Improvement Unit can input existing image generation technologies into AI and have the AI ​​perform technology improvements and develop applied technologies.

[0038] The investment department can provide funding to promising companies and offer technical support and business consulting. For example, the investment department can provide funding to promising companies. For example, the investment department can provide funding to promising companies based on criteria such as growth rate, technological innovation, and market share. For example, the investment department can provide technical support and business consulting. For example, the investment department can provide technical support such as technical consulting, training, and provision of technical documentation. For example, the investment department can provide business consulting such as marketing strategy, business plan, and fundraising support. By investing in promising companies, the investment department can promote the growth of these companies and contribute to the growth of the overall image generation AI market. Some or all of the processes described above in the investment department may be performed using AI, for example, or not. For example, the investment department can input an evaluation of promising companies into AI and have AI prioritize investments.

[0039] The comparison unit can analyze the user's past selection history and select the optimal comparison criteria. For example, the comparison unit proposes the optimal comparison criteria based on the characteristics of services the user has previously selected. For example, the comparison unit prioritizes displaying specific evaluation items from the user's past selection history. For example, the comparison unit analyzes the user's selection history and compares similar services. In this way, it supports the user's selection by providing the optimal comparison criteria based on the user's past selection history. Some or all of the above processing in the comparison unit may be performed using AI, for example, or without AI. For example, the comparison unit can input the user's past selection history into AI and have the AI ​​perform the selection of the optimal comparison criteria.

[0040] The comparison unit can filter based on the user's current needs and areas of interest. For example, the comparison unit filters services based on the technology field the user is currently interested in. For example, the comparison unit highlights specific evaluation criteria according to the user's current needs. For example, the comparison unit prioritizes displaying relevant services based on the user's areas of interest. This improves user satisfaction by providing the most suitable services based on the user's current needs and areas of interest. Some or all of the above processing in the comparison unit may be performed using AI, for example, or without AI. For example, the comparison unit can input the user's current needs and areas of interest into AI and have the AI ​​perform the filtering.

[0041] The comparison unit can prioritize comparing services that are highly relevant, taking into account the user's geographical location. For example, the comparison unit may prioritize displaying region-specific services based on the user's current location. For example, the comparison unit may suggest the most suitable service based on the user's geographical location. For example, the comparison unit may prioritize displaying nearby services, taking into account the user's location. This improves user convenience by providing the most suitable service based on the user's geographical location. Some or all of the above processing in the comparison unit may be performed using AI, for example, or without AI. For example, the comparison unit can input the user's geographical location into AI and have AI prioritize highly relevant services.

[0042] The comparison unit can analyze a user's social media activity and compare related services. For example, the comparison unit suggests related services based on the user's social media activity. For example, the comparison unit compares the best services by referring to the user's ratings on social media. For example, the comparison unit analyzes the user's interests on social media and displays related services preferentially. This improves user satisfaction by providing the best services based on the user's social media activity. Some or all of the above processing in the comparison unit may be performed using AI, for example, or without AI. For example, the comparison unit can input the user's social media activity into AI and have the AI ​​perform a comparison of related services.

[0043] The data collection unit can analyze past collected data and select the optimal data collection method. For example, the data collection unit can select the most effective data collection method based on past collected data. For example, the data collection unit can analyze past collected data and optimize the timing of data collection. For example, the data collection unit can narrow down the data to be collected by referring to past collected data. This improves the efficiency of information collection by providing the optimal data collection method based on past collected data. Some or all of the above processes in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input past collected data into AI and have the AI ​​select the optimal data collection method.

[0044] The data collection unit can perform filtering based on specific technology fields or market segments. For example, the data collection unit can prioritize the collection of relevant information based on specific technology fields. For example, the data collection unit can narrow down the collection target based on market segments. For example, the data collection unit can determine the priority of information based on specific technology fields or market segments. This improves user satisfaction by providing optimal information based on specific technology fields or market segments. 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 specific technology fields or market segments into the AI ​​and have the AI ​​perform the filtering.

[0045] The data collection unit can prioritize the collection of highly relevant information, taking into account the user's geographical location. For example, the data collection unit prioritizes the collection of region-specific information based on the user's current location. For example, the data collection unit collects optimal information based on the user's geographical location. For example, the data collection unit prioritizes the collection of nearby information, taking into account the user's location. This improves user convenience by providing optimal information based on the user's geographical location. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's geographical location information into AI and have AI perform the collection of highly relevant information.

[0046] The data collection unit can analyze the user's social media activity and collect relevant information. For example, the data collection unit can collect relevant information based on the user's social media activity. For example, the data collection unit can collect optimal information by referring to the user's social media ratings. For example, the data collection unit can analyze the user's social media interests and prioritize the collection of relevant information. This improves user satisfaction by providing optimal information based on the user's social media activity. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's social media activity into AI and have AI collect relevant information.

[0047] The information provider can adjust the level of detail provided based on the importance of the information. For example, the provider can provide highly important information in detail. For example, the provider can provide less important information concisely. The provider can adjust the level of detail provided according to the importance of the information. This improves user convenience by providing the optimal level of detail according to the importance of the information. Some or all of the above processing in the information provider may be performed using AI, for example, or without AI. For example, the information provider can input the importance of the information into the AI ​​and have the AI ​​perform the adjustment of the level of detail provided.

[0048] The information provider can apply different information provision algorithms depending on the category of information. For example, the provider might apply an information provision algorithm that includes a detailed explanation to technical information. For example, the provider might apply an information provision algorithm that includes a concise summary to market information. The provider might select the most suitable information provision algorithm depending on the category. This improves user convenience by providing the most suitable information provision algorithm according to the category of information. Some or all of the above processing in the information provider may be performed using AI, for example, or without AI. For example, the information provider can input the information category into the AI ​​and have the AI ​​perform the application of the information provision algorithm.

[0049] The information delivery unit can determine the priority of information delivery based on when the information was collected. For example, the delivery unit may prioritize the delivery of the latest information. For example, the delivery unit may postpone the delivery of older information. The delivery unit determines the priority of information delivery based on when the information was collected. This improves user convenience by providing the optimal priority according to when the information was collected. Some or all of the above processing in the delivery unit may be performed using AI, for example, or without AI. For example, the delivery unit can input the information collection period into the AI ​​and have the AI ​​perform the determination of the priority of information delivery.

[0050] The information delivery unit can adjust the order of delivery based on the relevance of the information. For example, the delivery unit may prioritize the delivery of highly relevant information. For example, the delivery unit may postpone the delivery of less relevant information. The delivery unit adjusts the order of delivery based on the relevance of the information. This improves user convenience by providing the optimal order according to the relevance of the information. Some or all of the above processing in the delivery unit may be performed using AI, for example, or without AI. For example, the delivery unit can input the relevance of the information into the AI ​​and have the AI ​​perform the adjustment of the delivery order.

[0051] The improvement unit can analyze past technical data and select the optimal improvement method. For example, the improvement unit selects the most effective improvement method based on past technical data. For example, the improvement unit analyzes past technical data and optimizes the timing of improvements. For example, the improvement unit narrows down the target of improvements by referring to past technical data. This improves the efficiency of technical improvements by providing the optimal improvement method based on past technical data. Some or all of the above processes in the improvement unit may be performed using AI, for example, or without AI. For example, the improvement unit can input past technical data into AI and have the AI ​​select the optimal improvement method.

[0052] The improvement unit can perform improvements based on specific technological fields or market segments. For example, the improvement unit prioritizes improving relevant technologies based on specific technological fields. For example, the improvement unit narrows down the scope of improvements based on market segments. For example, the improvement unit determines the priority of improvements based on specific technological fields or market segments. This improves the efficiency of technological improvements by providing optimal technological improvements based on specific technological fields or market segments. Some or all of the above processes in the improvement unit may be performed using AI, for example, or without AI. For example, the improvement unit can input specific technological fields or market segments into AI and have the AI ​​determine the priority of improvements.

[0053] The improvement unit can prioritize improving technologies that are highly relevant, taking into account the user's geographical location information. For example, the improvement unit prioritizes improving region-specific technologies based on the user's current location. For example, the improvement unit improves the optimal technology based on the user's geographical location information. For example, the improvement unit prioritizes improving nearby technologies, taking into account the user's location information. This improves the efficiency of technology improvement by providing optimal technology improvements based on the user's geographical location information. Some or all of the above processing in the improvement unit may be performed using AI, for example, or without AI. For example, the improvement unit can input the user's geographical location information into AI and have AI perform improvements on highly relevant technologies.

[0054] The improvement unit can analyze users' social media activities and improve related technologies. For example, the improvement unit improves related technologies based on the content of users' social media activities. For example, the improvement unit improves the optimal technologies by referring to users' ratings on social media. For example, the improvement unit analyzes users' interests on social media and prioritizes improving related technologies. This improves the efficiency of technology improvement by providing optimal technology improvements based on users' social media activities. Some or all of the above processing in the improvement unit may be performed using AI, for example, or without AI. For example, the improvement unit can input users' social media activities into AI and have the AI ​​perform improvements to related technologies.

[0055] The investment department can analyze past investment data and select the optimal investment method. For example, the investment department can select the most effective investment method based on past investment data. For example, the investment department can analyze past investment data and optimize the timing of investments. For example, the investment department can narrow down investment targets by referring to past investment data. This improves investment efficiency by providing the optimal investment method based on past investment data. Some or all of the above processes in the investment department may be performed using AI, for example, or without AI. For example, the investment department can input past investment data into AI and have the AI ​​select the optimal investment method.

[0056] The investment department can make investments based on specific technology fields or market segments. For example, the investment department may prioritize investments in relevant companies based on specific technology fields. For example, the investment department may narrow down investment targets based on market segments. For example, the investment department may determine investment priorities based on specific technology fields or market segments. This improves investment efficiency by providing optimal investments based on specific technology fields or market segments. Some or all of the above processes in the investment department may be performed using AI, for example, or not using AI. For example, the investment department can input specific technology fields or market segments into an AI and have the AI ​​perform the determination of investment priorities.

[0057] The investment department can prioritize investments in highly relevant companies by taking into account the user's geographical location. For example, the investment department can prioritize investments in geographically limited companies based on the user's current location. For example, the investment department can invest in the most suitable companies based on the user's geographical location. For example, the investment department can prioritize investments in nearby companies by taking into account the user's location. This improves investment efficiency by providing the most suitable companies based on the user's geographical location. Some or all of the above processes in the investment department may be performed using AI, for example, or without AI. For example, the investment department can input the user's geographical location into AI and have the AI ​​execute investments in highly relevant companies.

[0058] The investment department can analyze users' social media activity and invest in relevant companies. For example, the investment department invests in relevant companies based on the content of users' social media activity. For example, the investment department invests in the most suitable companies by referring to users' ratings on social media. For example, the investment department analyzes users' interests on social media and prioritizes investment in relevant companies. This improves investment efficiency by providing the most suitable companies based on users' social media activity. Some or all of the above processes in the investment department may be performed using AI, for example, or not using AI. For example, the investment department can input users' social media activity into AI and have the AI ​​execute investments in relevant companies.

[0059] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0060] The comparison unit can analyze the user's past selection history and select the optimal comparison criteria. For example, it can suggest the optimal comparison criteria based on the characteristics of services the user has previously selected. It can also prioritize the display of specific evaluation items based on the user's past selection history. Furthermore, it can analyze the user's selection history and compare similar services. This allows the unit to support the user's selection by providing the optimal comparison criteria based on the user's past selection history. Some or all of the above processing in the comparison unit may be performed using AI, for example, or without AI. For example, the comparison unit can input the user's past selection history into AI and have the AI ​​select the optimal comparison criteria.

[0061] The comparison unit can filter based on the user's current needs and areas of interest. For example, it can filter services based on the technology field the user is currently interested in. It can also highlight specific evaluation criteria according to the user's current needs. Furthermore, it can prioritize the display of relevant services based on the user's areas of interest. This improves user satisfaction by providing the most suitable services based on the user's current needs and areas of interest. Some or all of the above processing in the comparison unit may be performed using AI, for example, or without AI. For example, the comparison unit can input the user's current needs and areas of interest into an AI and have the AI ​​perform the filtering.

[0062] The comparison unit can prioritize the comparison of highly relevant services, taking into account the user's geographical location information. For example, it can prioritize displaying region-specific services based on the user's current location. It can also suggest the most suitable service based on the user's geographical location information. Furthermore, it can prioritize displaying nearby services, taking into account the user's location information. This improves user convenience by providing the most suitable service based on the user's geographical location information. Some or all of the above processing in the comparison unit may be performed using AI, for example, or without AI. For example, the comparison unit can input the user's geographical location information into AI and have the AI ​​prioritize highly relevant services.

[0063] The data collection unit can analyze past collected data and select the optimal data collection method. For example, it can select the most effective data collection method based on past data. It can also analyze past data to optimize the timing of data collection. Furthermore, it can narrow down the data to be collected based on past data. This improves the efficiency of information collection by providing the optimal data collection method based on past data. Some or all of the above processes in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input past data into AI and have the AI ​​select the optimal data collection method.

[0064] The information delivery unit can adjust the level of detail provided based on the importance of the information. For example, it can provide highly important information in detail, and less important information in a concise manner. Furthermore, it can adjust the level of detail provided according to the importance of the information. This improves user convenience by providing the optimal level of detail according to the importance of the information. Some or all of the above processing in the information delivery unit may be performed using AI, for example, or without AI. For example, the information delivery unit can input the importance of the information into the AI ​​and have the AI ​​perform the adjustment of the level of detail provided.

[0065] The following briefly describes the processing flow for example form 1.

[0066] Step 1: The comparison section provides service comparison information. For example, it allows users to compare the features and evaluations of image generation AI services, enabling them to compare the features and evaluations of each service when choosing a specific image generation AI service. Step 2: The collection unit gathers the latest information. For example, it quickly updates and provides the latest technology trends and market information so that users can always stay up-to-date. Step 3: The providing unit provides the information collected by the collection unit. For example, it can provide comparison reviews and rankings of services, allowing users to compare the features and ratings of each service when choosing a specific image generation AI service. Step 4: The improvement team improves existing technologies. For example, they investigate and research existing image generation technologies, develop further improvements and application technologies, and apply new algorithms to publicly available technologies to improve performance. Step 5: The investment department invests in promising companies. For example, it provides funding, technical support, and business consulting to startups developing new technologies and services in the field of AI image generation. The processes in the investment department may be performed using AI or not. For example, the evaluation of promising companies can be input into the AI, and the AI ​​can be used to prioritize investments.

[0067] (Example of form 2) The image generation AI market problem-solving platform according to an embodiment of the present invention is a system that provides service comparison information, collects the latest information, improves existing technologies, and invests in promising companies. This system aims to solve problems such as the proliferation of companies and services, the speed of information updates, and technical hurdles. For example, the platform that provides service comparison information provides comparative reviews and rankings of image generation AI services and provides the latest technology trends and market information with rapid updates. When users choose a specific image generation AI service, they can compare the features and evaluations of each service. In addition, by providing the latest technology information, it ensures that users are always up-to-date. Next, it improves and applies existing technologies. A team of experts is formed to investigate and research existing image generation technologies and develop further improvements and application technologies. For example, new algorithms are applied to publicly available technologies to improve performance. In addition, technical hurdles are lowered by providing customized solutions for companies. Furthermore, it invests in promising companies. Investments are made in companies that are judged to be "promising" through research. For example, funding, technical support, and business consulting are provided to startups that are developing new technologies and services in the image generation AI field. This will accelerate the growth of businesses and drive the overall growth of the image generation AI market. This platform will address the challenges of the image generation AI market, enabling businesses and researchers to select the optimal technologies and services based on reliable information. It also aims to promote technological advancement and application, building a sustainable ecosystem. Thus, the image generation AI market challenge-solving platform will allow businesses and researchers to select the optimal technologies and services based on reliable information.

[0068] The image generation AI market problem-solving platform according to this embodiment comprises a comparison unit, a collection unit, a provision unit, an improvement unit, and an investment unit. The comparison unit provides service comparison information. For example, the comparison unit can compare the features and evaluations of image generation AI services. The comparison unit allows users to compare the features and evaluations of each service when selecting a specific image generation AI service. The collection unit collects the latest information. For example, the collection unit quickly updates and provides the latest technology trends and market information. The collection unit ensures that users are always up-to-date. The provision unit provides the information collected by the collection unit. For example, the provision unit provides service comparison reviews and rankings. The provision unit allows users to compare the features and evaluations of each service when selecting a specific image generation AI service. The improvement unit improves existing technologies. For example, the improvement unit investigates and studies existing image generation technologies and develops further improvements and application technologies. The improvement unit applies new algorithms to published technologies to improve performance. The investment unit invests in promising companies. The investment department provides funding, technical support, and business consulting to startups developing new technologies and services in fields such as AI image generation. This enables the AI ​​image generation market's problem-solving platform to allow companies and researchers to select the optimal technologies and services based on reliable information. Some or all of the processes described above in the investment department may be performed using AI, or not. For example, the investment department could input evaluations of promising companies into AI and have the AI ​​prioritize investments.

[0069] The comparison section provides service comparison information. Specifically, it allows for detailed comparison of the features and evaluations of image generation AI services. For example, it compares a wide range of factors such as generation speed, generation quality, ease of use of the user interface, pricing, and support system for each service. This allows users to select the service that best suits their needs. The comparison section provides an interface for users to compare the features and evaluations of each service when choosing a specific image generation AI service. This interface is designed so that users can easily view detailed information about each service and compare them. For example, users can view the evaluations and reviews of a particular service and compare them with other services. The comparison section also collects user feedback and continuously updates service evaluations. This ensures that the comparison section always provides the latest information and helps users make the best choice. Furthermore, the comparison section also has a function that automatically analyzes the evaluation of each service using AI and recommends the best service to the user. For example, it can implement an algorithm in which the AI ​​recommends the best service based on the user's past selection history and evaluations. This allows the comparison section to provide personalized service comparison information tailored to the user's needs.

[0070] The data collection unit gathers the latest information. Specifically, it quickly updates and provides the latest technology trends and market information. The data collection unit gathers information from publicly available information on the internet, specialized databases, and industry news sites. For example, it collects the latest research papers and technology reports, industry event information, and company press releases and provides them to users. The data collection unit can use AI to collect and analyze information. For example, it can automatically collect information from the internet using web scraping technology and analyze the collected information using natural language processing technology. This allows the data collection unit to quickly extract important information from a vast amount of data and provide it to users. Furthermore, the data collection unit can filter information based on user interests and provide personalized information. For example, if a user is interested in a particular technology or company, it will prioritize providing relevant information based on that interest. This allows the data collection unit to ensure that users are always up-to-date and support quick and accurate decision-making.

[0071] The service provider provides information collected by the data collection department. Specifically, it provides service comparison reviews and rankings. The service provider provides information to help users compare the features and ratings of each service when choosing a specific image generation AI service. For example, it creates evaluations and rankings of each service based on the latest technology trends and market information collected by the data collection department and provides them to users. The service provider provides a user-friendly interface so that users can easily access the information. For example, it allows users to view service comparison reviews and rankings through websites and mobile apps. The service provider also collects user feedback and continuously improves the accuracy and reliability of the information it provides. This enables the service provider to help users choose the optimal service. Furthermore, the service provider can analyze the collected information using AI and provide personalized information to users. For example, it can implement an algorithm where AI recommends the optimal service based on the user's past selection history and evaluations. This enables the service provider to provide information tailored to the user's needs and help users make the best choices.

[0072] The Improvement Department improves existing technologies. Specifically, it investigates and researches existing image generation technologies and develops further improvements and applied technologies. The Improvement Department applies new algorithms to publicly available technologies to improve performance. For example, it incorporates the latest research results into existing image generation AI algorithms to improve generation quality and speed. The Improvement Department can also improve technologies based on user feedback. For example, if users are dissatisfied with a particular function or performance, it will use that feedback to make improvements and develop technologies that meet user needs. Furthermore, the Improvement Department can use AI to improve technologies. For example, by entrusting parameter tuning of generation AI and model optimization to AI, it can improve technologies efficiently and effectively. This allows the Improvement Department to constantly incorporate the latest technologies and improve performance. Furthermore, the Improvement Department can improve technologies in collaboration with other departments. For example, the Improvement Department improves technologies based on the latest technology trends and market information collected by the Data Collection Department. Also, the Improvement Department improves technologies based on user feedback collected by the Data Provision Department. This allows the Improvement Department to improve technologies efficiently and effectively in collaboration with other departments, thereby improving the overall system performance.

[0073] The Investment Department invests in promising companies. Specifically, it provides funding, technical support, and business consulting to startups developing new technologies and services in the field of AI image generation. For example, the Investment Department can input evaluations of promising companies into AI and have the AI ​​prioritize investments. The AI ​​analyzes a wide range of factors, such as a company's technological capabilities, business model, and market potential, to identify the optimal investment targets. This allows the Investment Department to invest efficiently and effectively. Furthermore, the Investment Department provides technical support and business consulting to portfolio companies to support their growth. For example, if a portfolio company faces technical challenges, the Investment Department provides specialized knowledge and resources to help solve those challenges. It also supports the improvement of the company's business model and the formulation of market strategies through business consulting. In this way, the Investment Department can promote the growth of portfolio companies and contribute to the development of the overall AI image generation market. In addition, the Investment Department continuously monitors the performance of portfolio companies and provides additional support as needed. For example, it provides additional funding or technical support depending on the company's growth status and market fluctuations. This will allow the investment department to fully support the success of its portfolio companies and contribute to solving the challenges in the image generation AI market.

[0074] The data collection unit can collect the latest technology trends and market information. For example, the data collection unit can collect the latest technology trends. For example, the data collection unit can collect technology trends such as AI, blockchain, and IoT. For example, the data collection unit can collect market information. For example, the data collection unit can collect market information such as competitive analysis, consumer trends, and sales data. This allows for the rapid collection and provision of the latest technology trends and market information. Some or all of the above-described processes in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the latest technology trends and market information into AI and have the AI ​​perform the information collection.

[0075] The service provider can provide comparative reviews and rankings of services based on the collected information. For example, the service provider can provide comparative reviews of services based on the collected information. For example, the service provider can provide comparative reviews of services based on criteria such as evaluation items, scoring methods, and the reliability of the reviews. For example, the service provider can provide rankings of services based on the collected information. For example, the service provider can provide rankings of services based on criteria such as evaluation standards, scoring methods, and the frequency of ranking updates. This allows users to compare the features and evaluations of each service when choosing a specific image generation AI service. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the collected information into an AI and have the AI ​​perform the provision of comparative reviews and rankings of services.

[0076] The Improvement Unit can investigate and research existing image generation technologies and develop further improvements and applied technologies. For example, the Improvement Unit can investigate and research existing image generation technologies. For example, the Improvement Unit can investigate and research image generation technologies such as GAN, VAE, and deep learning. For example, the Improvement Unit can develop further improvements and applied technologies. For example, the Improvement Unit can develop applied technologies such as medical image analysis, art generation, and autonomous driving. This lowers technical hurdles by improving existing technologies and developing applied technologies. Some or all of the above-mentioned processes in the Improvement Unit may be performed using AI, for example, or without AI. For example, the Improvement Unit can input existing image generation technologies into AI and have the AI ​​perform technology improvements and develop applied technologies.

[0077] The investment department can provide funding to promising companies and offer technical support and business consulting. For example, the investment department can provide funding to promising companies. For example, the investment department can provide funding to promising companies based on criteria such as growth rate, technological innovation, and market share. For example, the investment department can provide technical support and business consulting. For example, the investment department can provide technical support such as technical consulting, training, and provision of technical documentation. For example, the investment department can provide business consulting such as marketing strategy, business plan, and fundraising support. By investing in promising companies, the investment department can promote the growth of these companies and contribute to the growth of the overall image generation AI market. Some or all of the processes described above in the investment department may be performed using AI, for example, or not. For example, the investment department can input an evaluation of promising companies into AI and have AI prioritize investments.

[0078] The comparison unit can estimate the user's emotions and adjust the display method of the comparison results based on the estimated user emotions. For example, if the user is stressed, the comparison unit provides a simple and highly visible display method. For example, if the user is relaxed, the comparison unit provides a display method that includes detailed information. For example, if the user is in a hurry, the comparison unit provides a display method that gets straight to the point. This improves user convenience by providing the optimal display method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using 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 comparison unit may be performed using AI, for example, or without AI. For example, the comparison unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0079] The comparison unit can analyze the user's past selection history and select the optimal comparison criteria. For example, the comparison unit proposes the optimal comparison criteria based on the characteristics of services the user has previously selected. For example, the comparison unit prioritizes displaying specific evaluation items from the user's past selection history. For example, the comparison unit analyzes the user's selection history and compares similar services. In this way, it supports the user's selection by providing the optimal comparison criteria based on the user's past selection history. Some or all of the above processing in the comparison unit may be performed using AI, for example, or without AI. For example, the comparison unit can input the user's past selection history into AI and have the AI ​​perform the selection of the optimal comparison criteria.

[0080] The comparison unit can filter based on the user's current needs and areas of interest. For example, the comparison unit filters services based on the technology field the user is currently interested in. For example, the comparison unit highlights specific evaluation criteria according to the user's current needs. For example, the comparison unit prioritizes displaying relevant services based on the user's areas of interest. This improves user satisfaction by providing the most suitable services based on the user's current needs and areas of interest. Some or all of the above processing in the comparison unit may be performed using AI, for example, or without AI. For example, the comparison unit can input the user's current needs and areas of interest into AI and have the AI ​​perform the filtering.

[0081] The comparison unit can estimate the user's emotions and determine the priority of services to compare based on the estimated emotions. For example, if the user is stressed, the comparison unit will prioritize displaying simple and easy-to-use services. For example, if the user is relaxed, the comparison unit will prioritize displaying services that provide detailed information. For example, if the user is in a hurry, the comparison unit will prioritize displaying services that provide results quickly. This improves user convenience by prioritizing the display of the most suitable services according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the comparison unit may be performed using AI or not using AI. For example, the comparison unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0082] The comparison unit can prioritize comparing services that are highly relevant, taking into account the user's geographical location. For example, the comparison unit may prioritize displaying region-specific services based on the user's current location. For example, the comparison unit may suggest the most suitable service based on the user's geographical location. For example, the comparison unit may prioritize displaying nearby services, taking into account the user's location. This improves user convenience by providing the most suitable service based on the user's geographical location. Some or all of the above processing in the comparison unit may be performed using AI, for example, or without AI. For example, the comparison unit can input the user's geographical location into AI and have AI prioritize highly relevant services.

[0083] The comparison unit can analyze a user's social media activity and compare related services. For example, the comparison unit suggests related services based on the user's social media activity. For example, the comparison unit compares the best services by referring to the user's ratings on social media. For example, the comparison unit analyzes the user's interests on social media and displays related services preferentially. This improves user satisfaction by providing the best services based on the user's social media activity. Some or all of the above processing in the comparison unit may be performed using AI, for example, or without AI. For example, the comparison unit can input the user's social media activity into AI and have the AI ​​perform a comparison of related services.

[0084] The data collection unit can estimate the user's emotions and adjust the timing of information collection based on the estimated emotions. For example, the data collection unit increases the frequency of information collection when the user is relaxed. For example, the data collection unit decreases the frequency of information collection when the user is stressed. For example, the data collection unit collects information quickly when the user is in a hurry. This improves user convenience by collecting information at the optimal time according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI or not using AI. For example, the data collection unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0085] The data collection unit can analyze past collected data and select the optimal data collection method. For example, the data collection unit can select the most effective data collection method based on past collected data. For example, the data collection unit can analyze past collected data and optimize the timing of data collection. For example, the data collection unit can narrow down the data to be collected by referring to past collected data. This improves the efficiency of information collection by providing the optimal data collection method based on past collected data. Some or all of the above processes in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input past collected data into AI and have the AI ​​select the optimal data collection method.

[0086] The data collection unit can perform filtering based on specific technology fields or market segments. For example, the data collection unit can prioritize the collection of relevant information based on specific technology fields. For example, the data collection unit can narrow down the collection target based on market segments. For example, the data collection unit can determine the priority of information based on specific technology fields or market segments. This improves user satisfaction by providing optimal information based on specific technology fields or market segments. 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 specific technology fields or market segments into the AI ​​and have the AI ​​perform the filtering.

[0087] The data collection unit can estimate the user's emotions and determine the priority of information to collect based on the estimated emotions. For example, if the user is relaxed, the data collection unit will prioritize collecting detailed information. If the user is stressed, the data collection unit will prioritize collecting concise information. If the user is in a hurry, the data collection unit will prioritize collecting information that can be quickly retrieved. This improves user convenience by prioritizing the collection of information that is most relevant to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0088] The data collection unit can prioritize the collection of highly relevant information, taking into account the user's geographical location. For example, the data collection unit prioritizes the collection of region-specific information based on the user's current location. For example, the data collection unit collects optimal information based on the user's geographical location. For example, the data collection unit prioritizes the collection of nearby information, taking into account the user's location. This improves user convenience by providing optimal information based on the user's geographical location. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's geographical location information into AI and have AI perform the collection of highly relevant information.

[0089] The data collection unit can analyze the user's social media activity and collect relevant information. For example, the data collection unit can collect relevant information based on the user's social media activity. For example, the data collection unit can collect optimal information by referring to the user's social media ratings. For example, the data collection unit can analyze the user's social media interests and prioritize the collection of relevant information. This improves user satisfaction by providing optimal information based on the user's social media activity. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's social media activity into AI and have AI collect relevant information.

[0090] The information provider can estimate the user's emotions and adjust the way information is presented based on the estimated emotions. For example, if the user is relaxed, the information provider will provide detailed information. If the user is stressed, the information provider will provide concise information. If the user is in a hurry, the information provider will provide information quickly. This improves user convenience by providing the most appropriate presentation method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the information provider may be performed using AI or not using AI. For example, the information provider can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0091] The information provider can adjust the level of detail provided based on the importance of the information. For example, the provider can provide highly important information in detail. For example, the provider can provide less important information concisely. The provider can adjust the level of detail provided according to the importance of the information. This improves user convenience by providing the optimal level of detail according to the importance of the information. Some or all of the above processing in the information provider may be performed using AI, for example, or without AI. For example, the information provider can input the importance of the information into the AI ​​and have the AI ​​perform the adjustment of the level of detail provided.

[0092] The information provider can apply different information provision algorithms depending on the category of information. For example, the provider might apply an information provision algorithm that includes a detailed explanation to technical information. For example, the provider might apply an information provision algorithm that includes a concise summary to market information. The provider might select the most suitable information provision algorithm depending on the category. This improves user convenience by providing the most suitable information provision algorithm according to the category of information. Some or all of the above processing in the information provider may be performed using AI, for example, or without AI. For example, the information provider can input the information category into the AI ​​and have the AI ​​perform the application of the information provision algorithm.

[0093] The information provider can estimate the user's emotions and adjust the length of the information provided based on the estimated emotions. For example, if the user is relaxed, the information provider will provide more detailed information at a longer duration. If the user is stressed, the information provider will provide concise information at a shorter duration. If the user is in a hurry, the information provider will provide information quickly. This improves user convenience by providing the optimal length of information provision according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the information provider may be performed using AI or not using AI. For example, the information provider can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0094] The information delivery unit can determine the priority of information delivery based on when the information was collected. For example, the delivery unit may prioritize the delivery of the latest information. For example, the delivery unit may postpone the delivery of older information. The delivery unit determines the priority of information delivery based on when the information was collected. This improves user convenience by providing the optimal priority according to when the information was collected. Some or all of the above processing in the delivery unit may be performed using AI, for example, or without AI. For example, the delivery unit can input the information collection period into the AI ​​and have the AI ​​perform the determination of the priority of information delivery.

[0095] The information delivery unit can adjust the order of delivery based on the relevance of the information. For example, the delivery unit may prioritize the delivery of highly relevant information. For example, the delivery unit may postpone the delivery of less relevant information. The delivery unit adjusts the order of delivery based on the relevance of the information. This improves user convenience by providing the optimal order according to the relevance of the information. Some or all of the above processing in the delivery unit may be performed using AI, for example, or without AI. For example, the delivery unit can input the relevance of the information into the AI ​​and have the AI ​​perform the adjustment of the delivery order.

[0096] The improvement unit can estimate the user's emotions and adjust the method of technical improvement based on the estimated user emotions. For example, if the user is relaxed, the improvement unit will perform detailed technical improvements. For example, if the user is stressed, the improvement unit will perform simple technical improvements. For example, if the user is in a hurry, the improvement unit will perform rapid technical improvements. This improves the efficiency of technical improvement by providing the optimal method of technical improvement according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using 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 improvement unit may be performed using AI, for example, or not using AI. For example, the improvement unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0097] The improvement unit can analyze past technical data and select the optimal improvement method. For example, the improvement unit selects the most effective improvement method based on past technical data. For example, the improvement unit analyzes past technical data and optimizes the timing of improvements. For example, the improvement unit narrows down the target of improvements by referring to past technical data. This improves the efficiency of technical improvements by providing the optimal improvement method based on past technical data. Some or all of the above processes in the improvement unit may be performed using AI, for example, or without AI. For example, the improvement unit can input past technical data into AI and have the AI ​​select the optimal improvement method.

[0098] The improvement unit can perform improvements based on specific technological fields or market segments. For example, the improvement unit prioritizes improving relevant technologies based on specific technological fields. For example, the improvement unit narrows down the scope of improvements based on market segments. For example, the improvement unit determines the priority of improvements based on specific technological fields or market segments. This improves the efficiency of technological improvements by providing optimal technological improvements based on specific technological fields or market segments. Some or all of the above processes in the improvement unit may be performed using AI, for example, or without AI. For example, the improvement unit can input specific technological fields or market segments into AI and have the AI ​​determine the priority of improvements.

[0099] The improvement unit can estimate the user's emotions and determine the priority of technological improvements based on the estimated user emotions. For example, if the user is relaxed, the improvement unit will prioritize detailed technological improvements. For example, if the user is stressed, the improvement unit will prioritize simple technological improvements. For example, if the user is in a hurry, the improvement unit will prioritize rapid technological improvements. This improves the efficiency of technological improvements by providing the optimal priority of technological improvements according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the improvement unit may be performed using AI or not using AI. For example, the improvement unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0100] The improvement unit can prioritize improving technologies that are highly relevant, taking into account the user's geographical location information. For example, the improvement unit prioritizes improving region-specific technologies based on the user's current location. For example, the improvement unit improves the optimal technology based on the user's geographical location information. For example, the improvement unit prioritizes improving nearby technologies, taking into account the user's location information. This improves the efficiency of technology improvement by providing optimal technology improvements based on the user's geographical location information. Some or all of the above processing in the improvement unit may be performed using AI, for example, or without AI. For example, the improvement unit can input the user's geographical location information into AI and have AI perform improvements on highly relevant technologies.

[0101] The improvement unit can analyze users' social media activities and improve related technologies. For example, the improvement unit improves related technologies based on the content of users' social media activities. For example, the improvement unit improves the optimal technologies by referring to users' ratings on social media. For example, the improvement unit analyzes users' interests on social media and prioritizes improving related technologies. This improves the efficiency of technology improvement by providing optimal technology improvements based on users' social media activities. Some or all of the above processing in the improvement unit may be performed using AI, for example, or without AI. For example, the improvement unit can input users' social media activities into AI and have the AI ​​perform improvements to related technologies.

[0102] The investment unit can estimate the user's emotions and adjust its investment methods based on those emotions. For example, if the user is relaxed, the investment unit provides a detailed investment plan. If the user is stressed, the investment unit provides a concise investment plan. If the user is in a hurry, the investment unit provides a quick investment plan. This improves investment efficiency by providing the optimal investment method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the investment unit may be performed using AI or not. For example, the investment unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0103] The investment department can analyze past investment data and select the optimal investment method. For example, the investment department can select the most effective investment method based on past investment data. For example, the investment department can analyze past investment data and optimize the timing of investments. For example, the investment department can narrow down investment targets by referring to past investment data. This improves investment efficiency by providing the optimal investment method based on past investment data. Some or all of the above processes in the investment department may be performed using AI, for example, or without AI. For example, the investment department can input past investment data into AI and have the AI ​​select the optimal investment method.

[0104] The investment department can make investments based on specific technology fields or market segments. For example, the investment department may prioritize investments in relevant companies based on specific technology fields. For example, the investment department may narrow down investment targets based on market segments. For example, the investment department may determine investment priorities based on specific technology fields or market segments. This improves investment efficiency by providing optimal investments based on specific technology fields or market segments. Some or all of the above processes in the investment department may be performed using AI, for example, or not using AI. For example, the investment department can input specific technology fields or market segments into an AI and have the AI ​​perform the determination of investment priorities.

[0105] The investment unit can estimate the user's emotions and determine investment priorities based on those estimated emotions. For example, if the user is relaxed, the investment unit will prioritize providing a detailed investment plan. If the user is stressed, the investment unit will prioritize providing a concise investment plan. If the user is in a hurry, the investment unit will prioritize providing a quick investment plan. This improves investment efficiency by providing optimal investment priorities according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the investment unit may be performed using AI or not. For example, the investment unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0106] The investment department can prioritize investments in highly relevant companies by taking into account the user's geographical location. For example, the investment department can prioritize investments in geographically limited companies based on the user's current location. For example, the investment department can invest in the most suitable companies based on the user's geographical location. For example, the investment department can prioritize investments in nearby companies by taking into account the user's location. This improves investment efficiency by providing the most suitable companies based on the user's geographical location. Some or all of the above processes in the investment department may be performed using AI, for example, or without AI. For example, the investment department can input the user's geographical location into AI and have the AI ​​execute investments in highly relevant companies.

[0107] The investment department can analyze users' social media activity and invest in relevant companies. For example, the investment department invests in relevant companies based on the content of users' social media activity. For example, the investment department invests in the most suitable companies by referring to users' ratings on social media. For example, the investment department analyzes users' interests on social media and prioritizes investment in relevant companies. This improves investment efficiency by providing the most suitable companies based on users' social media activity. Some or all of the above processes in the investment department may be performed using AI, for example, or not using AI. For example, the investment department can input users' social media activity into AI and have the AI ​​execute investments in relevant companies.

[0108] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0109] The comparison unit can estimate the user's emotions and adjust the display method of the comparison results based on the estimated user emotions. For example, if the user is stressed, a simple and highly visible display method can be provided. If the user is relaxed, a display method including detailed information can be provided. Furthermore, if the user is in a hurry, a display method that gets straight to the point can be provided. This improves user convenience by providing the optimal display method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the comparison unit may be performed using AI, for example, or not using AI. For example, the comparison unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0110] The data collection unit can estimate the user's emotions and adjust the timing of information collection based on the estimated emotions. For example, if the user is relaxed, the frequency of information collection can be increased. Conversely, if the user is stressed, the frequency of information collection can be decreased. Furthermore, if the user is in a hurry, information can be collected quickly. This improves user convenience by collecting information at the optimal time according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data 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 have the generative AI perform emotion estimation.

[0111] The information provider can estimate the user's emotions and adjust the way information is presented based on the estimated emotions. For example, if the user is relaxed, detailed information can be provided. If the user is stressed, concise information can be provided. Furthermore, if the user is in a hurry, information can be provided quickly. This improves user convenience by providing the most appropriate presentation method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the information provider may be performed using AI, or not using AI. For example, the information provider can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0112] The improvement unit can estimate the user's emotions and adjust the method of technical improvement based on the estimated user emotions. For example, if the user is relaxed, detailed technical improvements can be made. If the user is stressed, simple technical improvements can be made. Furthermore, if the user is in a hurry, technical improvements can be made quickly. This improves the efficiency of technical improvement by providing the optimal method of technical improvement according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the improvement unit may be performed using AI, for example, or not using AI. For example, the improvement unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0113] The investment unit can estimate the user's emotions and adjust its investment methods based on those emotions. For example, if the user is relaxed, it can provide a detailed investment plan. If the user is stressed, it can provide a concise investment plan. Furthermore, if the user is in a hurry, it can provide an investment plan quickly. This improves investment efficiency by providing the optimal investment method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the investment unit may be performed using AI, or not using AI. For example, the investment unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0114] The comparison unit can analyze the user's past selection history and select the optimal comparison criteria. For example, it can suggest the optimal comparison criteria based on the characteristics of services the user has previously selected. It can also prioritize the display of specific evaluation items based on the user's past selection history. Furthermore, it can analyze the user's selection history and compare similar services. This allows the unit to support the user's selection by providing the optimal comparison criteria based on the user's past selection history. Some or all of the above processing in the comparison unit may be performed using AI, for example, or without AI. For example, the comparison unit can input the user's past selection history into AI and have the AI ​​select the optimal comparison criteria.

[0115] The comparison unit can filter based on the user's current needs and areas of interest. For example, it can filter services based on the technology field the user is currently interested in. It can also highlight specific evaluation criteria according to the user's current needs. Furthermore, it can prioritize the display of relevant services based on the user's areas of interest. This improves user satisfaction by providing the most suitable services based on the user's current needs and areas of interest. Some or all of the above processing in the comparison unit may be performed using AI, for example, or without AI. For example, the comparison unit can input the user's current needs and areas of interest into an AI and have the AI ​​perform the filtering.

[0116] The comparison unit can prioritize the comparison of highly relevant services, taking into account the user's geographical location information. For example, it can prioritize displaying region-specific services based on the user's current location. It can also suggest the most suitable service based on the user's geographical location information. Furthermore, it can prioritize displaying nearby services, taking into account the user's location information. This improves user convenience by providing the most suitable service based on the user's geographical location information. Some or all of the above processing in the comparison unit may be performed using AI, for example, or without AI. For example, the comparison unit can input the user's geographical location information into AI and have the AI ​​prioritize highly relevant services.

[0117] The data collection unit can analyze past collected data and select the optimal data collection method. For example, it can select the most effective data collection method based on past data. It can also analyze past data to optimize the timing of data collection. Furthermore, it can narrow down the data to be collected based on past data. This improves the efficiency of information collection by providing the optimal data collection method based on past data. Some or all of the above processes in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input past data into AI and have the AI ​​select the optimal data collection method.

[0118] The information delivery unit can adjust the level of detail provided based on the importance of the information. For example, it can provide highly important information in detail, and less important information in a concise manner. Furthermore, it can adjust the level of detail provided according to the importance of the information. This improves user convenience by providing the optimal level of detail according to the importance of the information. Some or all of the above processing in the information delivery unit may be performed using AI, for example, or without AI. For example, the information delivery unit can input the importance of the information into the AI ​​and have the AI ​​perform the adjustment of the level of detail provided.

[0119] The following briefly describes the processing flow for example form 2.

[0120] Step 1: The comparison section provides service comparison information. For example, it allows users to compare the features and evaluations of image generation AI services, enabling them to compare the features and evaluations of each service when choosing a specific image generation AI service. Step 2: The collection unit gathers the latest information. For example, it quickly updates and provides the latest technology trends and market information so that users can always stay up-to-date. Step 3: The providing unit provides the information collected by the collection unit. For example, it can provide comparison reviews and rankings of services, allowing users to compare the features and ratings of each service when choosing a specific image generation AI service. Step 4: The improvement team improves existing technologies. For example, they investigate and research existing image generation technologies, develop further improvements and application technologies, and apply new algorithms to publicly available technologies to improve performance. Step 5: The investment department invests in promising companies. For example, it provides funding, technical support, and business consulting to startups developing new technologies and services in the field of AI image generation. The processes in the investment department may be performed using AI or not. For example, the evaluation of promising companies can be input into the AI, and the AI ​​can be used to prioritize investments.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] Each of the multiple elements described above, including the comparison unit, collection unit, provision unit, improvement unit, and investment unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the comparison unit is implemented by the control unit 46A of the smart device 14 and compares the features and evaluations of image generation AI services. The collection unit is implemented by the specific processing unit 290 of the data processing unit 12 and quickly updates and provides the latest technology trends and market information. The provision unit is implemented by the control unit 46A of the smart device 14 and provides the information collected by the collection unit. The improvement unit is implemented by the specific processing unit 290 of the data processing unit 12 and investigates and studies existing image generation technologies and develops further improvements and application technologies. The investment unit is implemented by the specific processing unit 290 of the data processing unit 12 and invests in promising companies. The correspondence between each unit and the device or control unit is not limited to the examples described above and can be changed in various ways.

[0125] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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).

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.).

[0137] 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.

[0138] 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.

[0139] 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.

[0140] Each of the multiple elements described above, including the comparison unit, collection unit, provision unit, improvement unit, and investment unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the comparison unit is implemented by the control unit 46A of the smart glasses 214 and compares the features and evaluations of image generation AI services. The collection unit is implemented by the specific processing unit 290 of the data processing unit 12 and quickly updates and provides the latest technology trends and market information. The provision unit is implemented by the control unit 46A of the smart glasses 214 and provides the information collected by the collection unit. The improvement unit is implemented by the specific processing unit 290 of the data processing unit 12 and investigates and studies existing image generation technologies and develops further improvements and application technologies. The investment unit is implemented by the specific processing unit 290 of the data processing unit 12 and invests in promising companies. The correspondence between each unit and the device or control unit is not limited to the examples described above and can be changed in various ways.

[0141] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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).

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.).

[0153] 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.

[0154] 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.

[0155] 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.

[0156] Each of the multiple elements described above, including the comparison unit, collection unit, provision unit, improvement unit, and investment unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the comparison unit is implemented by the control unit 46A of the headset terminal 314 and compares the features and evaluations of image generation AI services. The collection unit is implemented by the specific processing unit 290 of the data processing unit 12 and quickly updates and provides the latest technology trends and market information. The provision unit is implemented by the control unit 46A of the headset terminal 314 and provides the information collected by the collection unit. The improvement unit is implemented by the specific processing unit 290 of the data processing unit 12 and investigates and studies existing image generation technologies and develops further improvements and application technologies. The investment unit is implemented by the specific processing unit 290 of the data processing unit 12 and invests in promising companies. The correspondence between each unit and the device or control unit is not limited to the examples described above and can be changed in various ways.

[0157] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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).

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.).

[0170] 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.

[0171] 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.

[0172] 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.

[0173] Each of the multiple elements described above, including the comparison unit, collection unit, provision unit, improvement unit, and investment unit, is implemented, for example, in at least one of the robot 414 and the data processing unit 12. For example, the comparison unit is implemented by the control unit 46A of the robot 414 and compares the features and evaluations of image generation AI services. The collection unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and quickly updates and provides the latest technology trends and market information. The provision unit is implemented, for example, by the control unit 46A of the robot 414 and provides the information collected by the collection unit. The improvement unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and investigates and studies existing image generation technologies and develops further improvements and application technologies. The investment unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and invests in promising companies. The correspondence between each unit and the device or control unit is not limited to the examples described above and can be changed in various ways.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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."

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] 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.

[0189] 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.

[0190] 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.

[0191] 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.

[0192] (Note 1) A comparison unit that provides service comparison information, The collection department gathers the latest information, A providing unit that provides the information collected by the aforementioned collection unit, An improvement section that enhances existing technology, It has an investment department that invests in promising companies. A system characterized by the following features. (Note 2) The aforementioned collection unit is Gather the latest technology trends and market information. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned supply unit is, Based on the collected information, we provide comparative reviews and rankings of services. The system described in Appendix 1, characterized by the features described herein. (Note 4) The improved part is, We investigate and research existing image generation technologies and develop further improvements and application technologies. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned investment department, We provide funding, technical support, and business consulting to promising companies. The system described in Appendix 1, characterized by the features described herein. (Note 6) The comparison unit is, It estimates the user's emotions and adjusts how comparison results are displayed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 7) The comparison unit is, Analyze the user's past selection history to select the optimal comparison criteria. The system described in Appendix 1, characterized by the features described herein. (Note 8) The comparison unit is, Filter based on the user's current needs and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 9) The comparison unit is, It estimates user sentiment and determines the priority of services based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 10) The comparison unit is, The system prioritizes comparing services based on their relevance, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 11) The comparison unit is, Analyze users' social media activity and compare related services. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned collection unit is It estimates the user's emotions and adjusts the timing of information collection based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned collection unit is Analyze past collected data and select the optimal collection method. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned collection unit is Filter based on specific technology fields or market segments. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned collection unit is It estimates the user's emotions and prioritizes the information to collect based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned collection unit is Prioritize the collection of highly relevant information, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned collection unit is Analyze users' social media activity and collect relevant information. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned supply unit is, The system estimates the user's emotions and adjusts the way information is presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned supply unit is, Adjust the level of detail provided based on the importance of the information. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned supply unit is, Apply different information delivery algorithms depending on the category of information. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned supply unit is, The system estimates the user's emotions and adjusts the length of information provided based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned supply unit is, Prioritizing information provision based on when the information was collected. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned supply unit is, The order in which information is provided will be adjusted based on its relevance. The system described in Appendix 1, characterized by the features described herein. (Note 24) The improved part is, We estimate user emotions and adjust the method of improving technology based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 25) The improved part is, Analyze past technical data and select the optimal improvement method. The system described in Appendix 1, characterized by the features described herein. (Note 26) The improved part is, Make improvements based on specific technology fields or market segments. The system described in Appendix 1, characterized by the features described herein. (Note 27) The improved part is, We estimate user emotions and prioritize technological improvements based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 28) The improved part is, Prioritize improving highly relevant technologies by considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 29) The improved part is, Analyze users' social media activity and improve related technologies. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned investment department, It estimates user sentiment and adjusts investment strategies based on that estimated sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned investment department, Analyze past investment data to select the optimal investment method. The system described in Appendix 1, characterized by the features described herein. (Note 32) The aforementioned investment department, Investing based on specific technology fields or market segments The system described in Appendix 1, characterized by the features described herein. (Note 33) The aforementioned investment department, It estimates user sentiment and determines investment priorities based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 34) The aforementioned investment department, Prioritize investments in highly relevant companies, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 35) The aforementioned investment department, Analyze users' social media activity and invest in relevant companies. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0193] 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 comparison unit that provides service comparison information, The collection department gathers the latest information, A providing unit that provides the information collected by the aforementioned collection unit, An improvement section that enhances existing technology, It has an investment department that invests in promising companies. A system characterized by the following features.

2. The aforementioned collection unit is Gather the latest technology trends and market information. The system according to feature 1.

3. The aforementioned supply unit is, Based on the collected information, we provide comparative reviews and rankings of services. The system according to feature 1.

4. The improved part is, We will investigate and research existing image generation technologies and develop further improvements and application technologies. The system according to feature 1.

5. The aforementioned investment department, We provide funding, technical support, and business consulting to promising companies. The system according to feature 1.

6. The comparison unit is, It estimates the user's emotions and adjusts how comparison results are displayed based on those estimated emotions. The system according to feature 1.

7. The comparison unit is, Analyze the user's past selection history to select the optimal comparison criteria. The system according to feature 1.

8. The comparison unit is, Filter based on the user's current needs and areas of interest. The system according to feature 1.

9. The comparison unit is, It estimates user sentiment and determines the priority of services based on the estimated user sentiment. The system according to feature 1.

10. The comparison unit is, The system prioritizes comparing services based on their relevance, taking into account the user's geographical location. The system according to feature 1.

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