system

Abstract

The system according to this embodiment aims to enable the efficient buying and selling of individuals' knowledge, skills, and experience. [Solution] The system according to this embodiment comprises a reception unit, an analysis unit, a service provision unit, and an identity verification unit. The reception unit inputs the user's knowledge, skills, and experience. The analysis unit analyzes the information input by the reception unit. The service provision unit provides services based on the information analyzed by the analysis unit. The identity verification unit performs identity verification to ensure the security of transactions.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, a system for efficiently trading personal knowledge, skills, and experience is not fully developed, and there is room for improvement.

[0005] The system according to the embodiment aims to enable efficient trading of personal knowledge, skills, and experience.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a reception unit, an analysis unit, a service provision unit, and an identity verification unit. The reception unit inputs the user's knowledge, skills, and experience. The analysis unit analyzes the information input by the reception unit. The service provision unit provides services based on the information analyzed by the analysis unit. The identity verification unit performs identity verification to ensure the security of transactions. [Effects of the Invention]

[0007] The system according to this embodiment allows for the efficient buying and selling of individuals' knowledge, skills, and experience. [Brief explanation of the drawing]

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

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

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

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

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

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

[0014] In the following embodiments, the labeled communication I / F (Interface) is an interface including a communication processor, an antenna, and the like. The communication I / F controls communication between 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 3, 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 skill marketplace system according to an embodiment of the present invention is a system that provides AI agent-specific services in a skill marketplace where individuals can buy and sell their knowledge, skills, and experience. In this skill marketplace system, users input their knowledge, skills, and experience into an AI agent, which analyzes the information and provides appropriate services, thereby ensuring transaction security and allowing users to receive high-quality services at a lower cost. For example, a user inputs their knowledge, skills, and experience into an AI agent. For example, they input their skills and experience in detail in categories such as design, website creation, video / music production, writing, and consulting. This information is input into the AI ​​agent. Next, the AI ​​agent analyzes the input information. Based on the user's skills and experience, the AI ​​agent generates a plan to provide appropriate services. For example, to a user with design skills, it proposes services such as website design or logo creation. Based on the generated plan, the AI ​​agent provides the service. For example, if a user requests website design, the AI ​​agent automatically creates the design and provides it to the user. In this way, users can leverage their skills and experience to provide services through the AI ​​agent. This mechanism ensures transaction security. AI agents can enhance the reliability of transactions through user identity verification, evaluation systems, and ranking systems. This allows users to use the service with peace of mind. Furthermore, users can receive high-quality services at a lower cost. Because AI agents provide optimal services based on the user's skills and experience, they can reduce costs while delivering high-quality service. For example, a user with design skills who requests website design through an AI agent can receive high-quality design at a lower cost than traditional methods. Additionally, users can compete in creating AI agents. They can leverage their skills and experience to create more advanced AI agents.This allows users to improve their own skills and provide excellent service to other users. By providing a skill marketplace specifically for AI agents, transaction security is ensured, and users can receive high-quality services at a lower cost. Furthermore, users can improve their own skills and provide excellent service to other users. This allows the skill marketplace system to efficiently buy and sell users' knowledge, skills, and experience, ensuring transaction security while providing high-quality services.

[0029] The skill marketplace system according to this embodiment comprises a reception unit, an analysis unit, a service provision unit, and an identity verification unit. The reception unit inputs the user's knowledge, skills, and experience. The user's knowledge, skills, and experience include, but are not limited to, technical skills, work experience, and specialized knowledge. The reception unit provides, for example, an interface for the user to input their skills and experience in detail. The reception unit can also store the information entered by the user in a database. For example, the reception unit can store the information entered by the user in cloud storage and make it accessible as needed. The analysis unit analyzes the information entered by the reception unit. The analysis is performed by, for example, data mining, statistical analysis, machine learning algorithms, etc., but is not limited to these methods. For example, the analysis unit generates a plan to provide appropriate services based on the user's skills and experience. The analysis unit can also evaluate skills and experience based on the user's input information. For example, the analysis unit analyzes the user's skill set and determines which areas of service provision are optimal. The service provision unit provides services based on the information analyzed by the analysis unit. Services include, but are not limited to, consulting services, training programs, and technical support. For example, the service provider can automatically perform the services requested by the user and provide the results. The service provider can also provide optimal services based on the user's skills and experience. For example, the service provider can propose a customized service plan based on the user's skill set. The identity verification unit verifies the user's identity to ensure the security of transactions. Identity verification can be performed by, but is not limited to, presenting identification documents, biometric authentication, or two-factor authentication. For example, the identity verification unit can automatically verify the user's submitted identification documents. The identity verification unit can also perform identity verification using the user's biometric data. For example, the identity verification unit can perform identity verification using the user's fingerprints or facial recognition data. This enables the skill marketplace system according to the embodiment to efficiently input, analyze, provide, and verify the user's knowledge, skills, and experience.

[0030] The reception desk inputs the user's knowledge, skills, and experience. This includes, but is not limited to, technical skills, work experience, and specialized knowledge. The reception desk provides an interface for users to input their skills and experience in detail. Specifically, users can input information using text boxes and dropdown menus via a web browser or mobile application. This allows users to describe their skills and experience in detail and register them in the system. The reception desk can also store the user-entered information in a database. For example, it can store user-entered information in cloud storage and make it accessible as needed. Cloud storage incorporates encryption technology and backup functions to ensure data security and availability. Furthermore, the reception desk has the ability to automatically categorize and tag user-entered information. This allows subsequent analysis and delivery departments to process the data efficiently. For example, if a user enters skills such as "programming" or "project management," the reception desk categorizes these skills appropriately and assigns relevant tags. This streamlines data management across the entire system, enabling the provision of more appropriate services to users.

[0031] The analysis department analyzes the information entered by the reception department. Analysis is performed using methods such as data mining, statistical analysis, and machine learning algorithms, but is not limited to these examples. Specifically, the analysis department generates a plan to provide appropriate services based on the user's skills and experience. For example, it can use data mining techniques to extract common patterns and trends from the user's skill set and propose an optimal service plan. It can also use machine learning algorithms to compare the user's past data with data from other users to evaluate skills and experience. For example, the analysis department analyzes the user's skill set to determine which areas of service provision are optimal. Furthermore, the analysis department can evaluate skills and experience based on the user's input information. For example, the analysis department analyzes the user's skill set to determine which areas of service provision are optimal. AI-based analysis utilizes natural language processing techniques to analyze text data entered by the user and extract details of skills and experience. This allows the analysis department to accurately understand the user's skills and experience and generate an optimal service plan. Furthermore, the analysis department can provide advice on future career paths and skill development based on the user's skills and experience. For example, the analytics unit compares a user's current skill set with market demand and suggests which skills should be strengthened. This allows the analytics unit to provide users with more valuable information and maximize the effectiveness of the skill marketplace system.

[0032] The service provider delivers services based on information analyzed by the analysis provider. These services include, but are not limited to, consulting services, training programs, and technical support. Specifically, the service provider automatically executes the services requested by the user and provides the results. For example, if a user desires training on a specific technology, the service provider automatically schedules the training program and notifies the user. The service provider can also provide optimal services based on the user's skills and experience. For example, the service provider proposes a customized service plan based on the user's skill set, ensuring the user receives the service best suited to their needs. Furthermore, the service provider can monitor the service delivery status in real time and make adjustments as needed. For example, if a user encounters a problem during a training program, the service provider can respond immediately and provide appropriate support. The service provider can also collect user feedback and continuously improve the quality of its services. For example, it can collect user evaluations and opinions on the services received and revise the service content based on this feedback. This allows the service provider to continue providing high-quality services to users. Additionally, the service provider can integrate with other systems and platforms to provide users with a wider range of services. For example, by partnering with external experts and trainers, the service provider can offer users specialized consulting and training. This allows the service provider to meet the diverse needs of users and maximize the value of the skill marketplace system.

[0033] The Identity Verification Department verifies the user's identity to ensure the security of transactions. Identity verification is performed using methods such as, but is not limited to, presentation of identification documents, biometric authentication, and two-factor authentication. Specifically, the Identity Verification Department automatically verifies the identification documents submitted by the user. For example, if a user submits a driver's license or passport, the Identity Verification Department scans these documents and compares them against a database to verify the user's identity. The Identity Verification Department can also verify the user's identity using their biometric data. For example, it can use the user's fingerprints or facial recognition data to verify their identity. This allows users to complete identity verification quickly and reliably. Furthermore, the Identity Verification Department can enhance security by implementing two-factor authentication. For example, when a user logs in, they are required to enter a one-time passcode sent to their smartphone in addition to their password. This prevents unauthorized access and ensures the security of transactions. The Identity Verification Department also takes measures to securely manage user identity information and protect privacy. For example, identity information is encrypted and stored on a secure server. This minimizes the risk of unauthorized access to users' personal information. Furthermore, the identity verification department can conduct regular security audits to detect and improve system vulnerabilities. This allows the identity verification department to always maintain the latest security measures and ensure user safety.

[0034] The service provider can offer optimal services based on the user's skills and experience. For example, the service provider can analyze the user's skill set and propose the optimal service plan. For instance, the service provider can propose services such as website design and logo creation to a user with design skills. The service provider can also offer customized services based on the user's experience. For example, the service provider can offer business consulting services to a user with consulting experience. The service provider can also offer personalized services based on the user's needs. For example, the service provider can introduce a specialist with the optimal skill set according to the user's project. This improves the quality of service by providing optimal services based on the user's skills and experience. Some or all of the above processes in the service provider may be performed using AI, for example, or not. For example, the service provider can offer services using an AI model that takes the user's skill set as input and outputs the optimal service plan.

[0035] The identity verification unit can verify the user's identity. For example, the identity verification unit can automatically verify the user's submitted identification document. For example, the identity verification unit can analyze the image of the identification document to check for forgery. The identity verification unit can also verify the user's identity using biometric authentication. For example, the identity verification unit can verify the user's identity using the user's fingerprint or facial recognition data. The identity verification unit can also verify the user's identity using two-factor authentication. For example, the identity verification unit can verify the user's identity by combining the user's password with an authentication code sent to their smartphone. This ensures the security of transactions by verifying the user's identity. Some or all of the above processes in the identity verification unit may be performed using AI, for example, or without AI. For example, the identity verification unit can input the image data of the identification document into a generating AI and have the generating AI check for forgery.

[0036] The service provider can enhance the reliability of transactions through an evaluation system. For example, the service provider can collect user feedback and improve transaction reliability through the evaluation system. For example, the service provider can collect user evaluations of services provided and calculate a confidence score. The service provider can also enhance transaction reliability through performance evaluations. For example, the service provider can analyze a user's service provision history and perform a performance evaluation. The service provider can also determine a user's rank based on their confidence score. For example, the service provider can assign a high rank to users with high confidence scores. This improves transaction reliability through the evaluation system. Some or all of the above processes in the service provider may be performed using AI, for example, or not using AI. For example, the service provider can input user feedback data into a generating AI and have the generating AI calculate the confidence score.

[0037] The service provider can enhance the reliability of transactions through a ranking system. For example, the service provider can improve transaction reliability by assigning ranks based on user evaluation criteria. For example, the service provider can determine ranks based on users' service provision history and feedback. The service provider can also evaluate user reliability through the ranking process. For example, the service provider can assign a rank to users who meet certain evaluation criteria. The service provider can also continuously evaluate user reliability through the ranking system. For example, the service provider can periodically review user evaluations and update ranks. This improves transaction reliability through the ranking system. Some or all of the above processes in the service provider may be performed using AI, for example, or not using AI. For example, the service provider can input user evaluation data into a generating AI and have the generating AI determine the ranks.

[0038] The analysis unit can generate plans to provide appropriate services based on the user's skills and experience. For example, the analysis unit can analyze the user's skill set and generate an optimal service plan. For instance, it might propose service plans such as website design or logo creation to a user with design skills. The analysis unit can also generate customized service plans based on the user's experience. For example, it might generate a business consulting service plan for a user with consulting experience. The analysis unit can also generate personalized service plans based on the user's needs. For example, it might generate a plan to introduce experts with the most suitable skill sets for the user's project. This improves the quality of service by generating appropriate service plans based on the user's skills and experience. Some or all of the above-described processes in the analysis unit may be performed using, for example, generative AI, or without generative AI. For example, the analysis unit can generate plans using a generative AI model that takes the user's skill set as input and outputs an optimal service plan.

[0039] The service provider may include a competition section where users can compete in their skills to create AI agents. For example, the service provider could host a competition where users can create AI agents using their skills and experience. For example, the service provider could host a contest to compete in AI agent creation skills and award prizes to users who create outstanding AI agents. The service provider could also provide training programs for users to create AI agents. For example, the service provider could offer online courses to improve AI agent creation skills. The service provider could also provide resources for users to create AI agents. For example, the service provider could provide the tools and datasets necessary for creating AI agents. This would allow users to improve their skills by competing in AI agent creation. Some or all of the above processes in the service provider may be performed using, for example, generative AI, or not using generative AI. For example, the service provider could evaluate users' AI agent creation skills and determine the competition results based on generative AI.

[0040] The reception desk can analyze the user's past input history and select the optimal input method. For example, the reception desk can prioritize suggesting input methods (voice, text, etc.) that the user has frequently used in the past. For example, the reception desk can predict and suggest an input method to be used during a specific time period based on the user's past input history. The reception desk can also customize the optimal input method based on information the user has entered in the past. For example, the reception desk can analyze the user's past input data and suggest the optimal input method. In this way, the reception desk can suggest the optimal input method by analyzing past input history. Some or all of the above processing in the reception desk may be performed using AI, for example, or not using AI. For example, the reception desk can input the user's past input data into a generating AI and have the generating AI select the optimal input method.

[0041] The reception system can filter the input of knowledge, skills, and experience based on the user's current projects and areas of interest. For example, the reception system can prioritize inputting skills and experience related to the user's current project. For example, the reception system can filter and input relevant knowledge and skills based on the user's areas of interest. The reception system can also dynamically filter the necessary skills and experience according to the progress of the user's project. For example, the reception system can monitor the progress of the user's project in real time and suggest the necessary skills and experience. This allows for the input of highly relevant information by filtering based on the current project and areas of interest. Some or all of the above processing in the reception system may be performed using AI, for example, or not using AI. For example, the reception system can input the user's project data into a generating AI and have the generating AI perform the filtering.

[0042] The reception desk can prioritize inputting highly relevant information based on the user's geographical location when inputting knowledge, skills, and experience. For example, if the user is in a specific region, the reception desk will prioritize inputting skills and experience related to that region. For example, the reception desk can filter and input relevant knowledge and skills based on the user's current location. The reception desk can also suggest the optimal input method based on the user's geographical location. For example, the reception desk can acquire the user's geographical location in real time and suggest the optimal input method. This enables efficient information gathering by inputting highly relevant information based on geographical location. Some or all of the above processing in the reception desk may be performed using AI, for example, or without AI. For example, the reception desk can input the user's geographical location data into a generating AI and have the generating AI input highly relevant information.

[0043] The reception desk can analyze the user's social media activity and input relevant information when inputting knowledge, skills, and experience. For example, the reception desk can extract and input relevant skills and experience from the user's social media activity. For example, the reception desk can input relevant knowledge and skills based on the user's interests on social media. The reception desk can also analyze the content of the user's social media posts and suggest the optimal input method. For example, the reception desk can analyze the user's social media post data and suggest the optimal input method. This allows for efficient input of relevant information by analyzing social media activity. Some or all of the above processing in the reception desk may be performed using AI, for example, or without AI. For example, the reception desk can input the user's social media data into a generating AI and have the generating AI input the relevant information.

[0044] The analysis unit can adjust the level of detail of the analysis based on the importance of the information during the analysis. For example, the analysis unit can perform a detailed analysis on important information and simplify other information. For example, the analysis unit can adjust the depth of the analysis according to the importance of the information. The analysis unit can also prioritize the analysis of highly important information and provide detailed results. For example, the analysis unit can prioritize the analysis of information with high business impact and provide a detailed report. This allows for efficient analysis by adjusting the level of detail of the analysis based on the importance of the information. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input information importance data into a generating AI and have the generating AI perform the adjustment of the level of detail of the analysis.

[0045] The analysis unit can apply different analysis algorithms depending on the category of information during analysis. For example, the analysis unit can apply a design-specific analysis algorithm to design-related information. For example, the analysis unit can apply a consulting-specific analysis algorithm to consulting-related information. The analysis unit can also apply a writing-specific analysis algorithm to writing-related information. For example, the analysis unit can use a writing-specific algorithm to analyze information about users with writing skills. By applying analysis algorithms according to the category of information, highly accurate analysis becomes possible. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input information category data into a generating AI and have the generating AI select the analysis algorithm to apply.

[0046] The analysis unit can determine the priority of analysis based on the information submission date during the analysis. For example, the analysis unit may prioritize the analysis of the most recent information and provide results quickly. For example, the analysis unit may postpone the analysis of older information. The analysis unit can also adjust the order of analysis based on the submission date. For example, the analysis unit may dynamically adjust the order of analysis based on the submission date. This enables efficient analysis by determining the priority of analysis based on the information submission date. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input information submission date data into a generating AI and have the generating AI perform the determination of analysis priority.

[0047] The analysis unit can adjust the order of analysis based on the relevance of the information during the analysis. For example, the analysis unit may prioritize the analysis of highly relevant information and provide results. For example, the analysis unit may postpone the analysis of less relevant information. The analysis unit can also dynamically adjust the order of analysis based on the relevance of the information. For example, the analysis unit can dynamically adjust the order of analysis based on the relevance of the information. This allows for efficient analysis by adjusting the order of analysis based on the relevance of the information. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input information relevance data into a generating AI and have the generating AI perform the adjustment of the analysis order.

[0048] The service provider can adjust the level of detail provided based on the importance of the service at the time of delivery. For example, the service provider can provide detailed explanations for important services and simplify other services. For example, the service provider can adjust the depth of the delivery according to the importance of the service. The service provider can also prioritize providing highly important services and provide detailed explanations. For example, the service provider can prioritize providing services with a high business impact and provide detailed explanations. This allows for efficient service delivery by adjusting the level of detail based on the importance of the service. Some or all of the above processing in the service provider may be performed using AI, for example, or not using AI. For example, the service provider can input service importance data into a generating AI and have the generating AI perform the adjustment of the level of detail of the delivery.

[0049] The service provider can apply different service provision algorithms depending on the service category at the time of provision. For example, the service provider can apply a design-specific service provision algorithm to design-related services. For example, the service provider can apply a consulting-specific service provision algorithm to consulting-related services. The service provider can also apply a writing-specific service provision algorithm to writing-related services. For example, the service provider can provide services to users with writing skills using a writing-specific algorithm. By applying service provision algorithms according to the service category, it becomes possible to provide highly accurate services. 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 service category data into a generating AI and have the generating AI select the service provision algorithm to apply.

[0050] The service provider can determine the priority of service delivery based on the submission date. For example, the service provider may prioritize the most recent services and provide results quickly. For example, the service provider may postpone the delivery of older services. The service provider can also adjust the order of delivery based on the submission date. For example, the service provider may dynamically adjust the order of delivery based on the submission date. This enables efficient service delivery by determining the priority of service delivery based on the submission date. Some or all of the above processing in the service provider may be performed using AI, for example, or not using AI. For example, the service provider can input service submission date data into a generating AI and have the generating AI perform the determination of the delivery priority.

[0051] The service delivery unit can adjust the order of service delivery based on the relevance of the services. For example, the service delivery unit may prioritize providing highly relevant services and deliver results. For example, the service delivery unit may postpone providing less relevant services. The service delivery unit can also dynamically adjust the order of service delivery based on the relevance of the services. For example, the service delivery unit dynamically adjusts the order of service delivery based on the relevance of the services. This enables efficient service delivery by adjusting the order of service delivery based on the relevance of the services. Some or all of the above processing in the service delivery unit may be performed using AI, for example, or without AI. For example, the service delivery unit can input service relevance data into a generating AI and have the generating AI perform the adjustment of the order of service delivery.

[0052] The identity verification unit can select the most suitable verification method by referring to the user's past transaction history during identity verification. For example, if the user has traded frequently in the past, the identity verification unit can provide a simplified identity verification procedure. For example, the identity verification unit can select a highly reliable verification method from the user's past transaction history. The identity verification unit can also propose the most suitable identity verification procedure based on the user's transaction history. For example, the identity verification unit can analyze the user's transaction history and propose the most suitable verification method. This allows the unit to propose the most suitable identity verification method by referring to past transaction history. Some or all of the above processing in the identity verification unit may be performed using AI, for example, or without AI. For example, the identity verification unit can input the user's transaction history data into a generating AI and have the generating AI select the most suitable verification method.

[0053] The identity verification unit can customize the verification process based on the user's current situation during identity verification. For example, if the user is in a hurry, the identity verification unit can provide a quick verification procedure. For example, if the user is relaxed, the identity verification unit can provide a detailed verification procedure. The identity verification unit can also suggest the most suitable verification procedure based on the user's current situation. For example, the identity verification unit can suggest the most suitable verification method considering the user's current activities and environmental conditions. This allows for efficient identity verification by customizing the verification method based on the current situation. Some or all of the above-described processes in the identity verification unit may be performed using AI, for example, or not using AI. For example, the identity verification unit can input the user's current situation data into a generating AI and have the generating AI perform the customization of the verification method.

[0054] The identity verification unit can select the most appropriate verification method based on the user's geographical location information during identity verification. For example, if the user is in a specific region, the identity verification unit will prioritize verification procedures related to that region. For example, the identity verification unit will filter and perform relevant verification procedures based on the user's current location. The identity verification unit can also propose the most appropriate verification method based on the user's geographical location information. For example, the identity verification unit can acquire the user's geographical location information in real time and propose the most appropriate verification method. This enables efficient identity verification by selecting the most appropriate verification method based on geographical location information. Some or all of the above-described processes in the identity verification unit may be performed using AI, for example, or without AI. For example, the identity verification unit can input the user's geographical location data into a generating AI and have the generating AI select the most appropriate verification method.

[0055] The identity verification unit can analyze a user's social media activity and propose verification methods during identity verification. For example, the identity verification unit can extract relevant verification procedures from the user's social media activity. For example, the identity verification unit can perform relevant verification procedures based on the user's interests on social media. The identity verification unit can also analyze the content of the user's social media posts and propose the most suitable verification method. For example, the identity verification unit can analyze the user's social media post data and propose the most suitable verification method. In this way, by analyzing social media activity, the optimal verification method can be proposed. Some or all of the above processing in the identity verification unit may be performed using AI, for example, or without AI. For example, the identity verification unit can input the user's social media data into a generating AI and have the generating AI execute the proposal of verification methods.

[0056] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0057] The analysis unit can analyze a user's past trading history and select the optimal analysis algorithm. For example, the analysis unit can analyze patterns of successful past trades and apply the optimal analysis algorithm to new trades with similar patterns. The analysis unit can also suggest the optimal analysis method for specific skills and experience based on the user's past trading history. For example, the analysis unit can select an algorithm that performs a more detailed analysis on skills in which the user has received high ratings in the past. This improves the accuracy of the analysis by selecting the optimal analysis algorithm based on past trading history. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the user's trading history data into a generating AI and have the generating AI select the optimal analysis algorithm.

[0058] The identity verification unit can analyze a user's past identity verification history and select the most suitable verification method. For example, the identity verification unit can analyze the success rate of identity verification methods used by the user in the past and propose the method with the highest success rate. Furthermore, the identity verification unit can select the most suitable verification method in a specific situation based on the user's past identity verification history. For example, the identity verification unit can propose a verification method for a similar region based on the identity verification method the user has used in a specific region in the past. This improves the efficiency of identity verification by selecting the most suitable method based on past identity verification history. Some or all of the above-described processes in the identity verification unit may be performed using AI, for example, or without AI. For example, the identity verification unit can input the user's identity verification history data into a generating AI and have the generating AI select the most suitable verification method.

[0059] The analysis unit can suggest potential collaborations with other users based on the user's skills and experience. For example, the analysis unit can analyze the user's skill set and suggest other users with complementary skills. It can also analyze patterns of successful past collaborations based on the user's experience and suggest users with similar patterns. For example, the analysis unit can suggest collaboration partners suitable for similar projects based on data from successful past projects. This improves the project success rate by suggesting collaboration possibilities based on the user's skills and experience. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the user's skill set data into a generating AI and have the generating AI suggest collaboration partners.

[0060] The reception desk can analyze user input data in real time and check for consistency in the input content. For example, the reception desk can display a warning if the skills or experience entered by the user are inconsistent. The reception desk can also verify the consistency of the data entered by the user and suggest ways to supplement any missing information. For example, the reception desk can prompt the user to enter relevant experience and knowledge based on the skill set they entered. This ensures accurate information collection by checking the consistency of the input data. Some or all of the above processes in the reception desk may be performed using AI, for example, or not using AI. For example, the reception desk can input user input data into a generating AI and have the generating AI perform consistency checks.

[0061] The analysis unit can propose future career paths based on the user's skills and experience. For example, the analysis unit can analyze the user's skill set and propose skills that will be useful for their future career. Furthermore, based on the user's experience, the analysis unit can analyze patterns of successful career paths in the past and propose career paths with similar patterns. For example, based on data from projects the user has successfully completed in the past, the analysis unit can propose skills and experiences suitable for their future career. This improves the success rate of careers by proposing future career paths based on the user's skills and experience. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the user's skill set data into a generating AI and have the generating AI propose career paths.

[0062] The following briefly describes the processing flow for example form 1.

[0063] Step 1: The reception desk inputs the user's knowledge, skills, and experience. This includes, for example, technical skills, work experience, and specialized knowledge. The reception desk provides an interface for users to input their skills and experience in detail, and the entered information can be saved to a database or cloud storage. Step 2: The analysis unit analyzes the information entered by the reception unit. The analysis is performed using methods such as data mining, statistical analysis, and machine learning algorithms. Based on the user's skills and experience, the analysis unit generates a plan to provide appropriate services and evaluates the user's skills and experience. Step 3: The service provider delivers services based on the information analyzed by the analysis provider. These services include consulting services, training programs, and technical support. The service provider automatically executes the services requested by the user and provides the results. It also proposes a customized service plan based on the user's skills and experience. Step 4: The Identity Verification Unit verifies the user's identity to ensure the security of the transaction. Identity verification is performed using methods such as presentation of identification documents, biometric authentication, and two-factor authentication. The Identity Verification Unit automatically verifies the identification documents submitted by the user and performs identity verification using biometric data.

[0064] (Example of form 2) The skill marketplace system according to an embodiment of the present invention is a system that provides AI agent-specific services in a skill marketplace where individuals can buy and sell their knowledge, skills, and experience. In this skill marketplace system, users input their knowledge, skills, and experience into an AI agent, which analyzes the information and provides appropriate services, thereby ensuring transaction security and allowing users to receive high-quality services at a lower cost. For example, a user inputs their knowledge, skills, and experience into an AI agent. For example, they input their skills and experience in detail in categories such as design, website creation, video / music production, writing, and consulting. This information is input into the AI ​​agent. Next, the AI ​​agent analyzes the input information. Based on the user's skills and experience, the AI ​​agent generates a plan to provide appropriate services. For example, to a user with design skills, it proposes services such as website design or logo creation. Based on the generated plan, the AI ​​agent provides the service. For example, if a user requests website design, the AI ​​agent automatically creates the design and provides it to the user. In this way, users can leverage their skills and experience to provide services through the AI ​​agent. This mechanism ensures transaction security. AI agents can enhance the reliability of transactions through user identity verification, evaluation systems, and ranking systems. This allows users to use the service with peace of mind. Furthermore, users can receive high-quality services at a lower cost. Because AI agents provide optimal services based on the user's skills and experience, they can reduce costs while delivering high-quality service. For example, a user with design skills who requests website design through an AI agent can receive high-quality design at a lower cost than traditional methods. Additionally, users can compete in creating AI agents. They can leverage their skills and experience to create more advanced AI agents.This allows users to improve their own skills and provide excellent service to other users. By providing a skill marketplace specifically for AI agents, transaction security is ensured, and users can receive high-quality services at a lower cost. Furthermore, users can improve their own skills and provide excellent service to other users. This allows the skill marketplace system to efficiently buy and sell users' knowledge, skills, and experience, ensuring transaction security while providing high-quality services.

[0065] The skill marketplace system according to this embodiment comprises a reception unit, an analysis unit, a service provision unit, and an identity verification unit. The reception unit inputs the user's knowledge, skills, and experience. The user's knowledge, skills, and experience include, but are not limited to, technical skills, work experience, and specialized knowledge. The reception unit provides, for example, an interface for the user to input their skills and experience in detail. The reception unit can also store the information entered by the user in a database. For example, the reception unit can store the information entered by the user in cloud storage and make it accessible as needed. The analysis unit analyzes the information entered by the reception unit. The analysis is performed by, for example, data mining, statistical analysis, machine learning algorithms, etc., but is not limited to these methods. For example, the analysis unit generates a plan to provide appropriate services based on the user's skills and experience. The analysis unit can also evaluate skills and experience based on the user's input information. For example, the analysis unit analyzes the user's skill set and determines which areas of service provision are optimal. The service provision unit provides services based on the information analyzed by the analysis unit. Services include, but are not limited to, consulting services, training programs, and technical support. For example, the service provider can automatically perform the services requested by the user and provide the results. The service provider can also provide optimal services based on the user's skills and experience. For example, the service provider can propose a customized service plan based on the user's skill set. The identity verification unit verifies the user's identity to ensure the security of transactions. Identity verification can be performed by, but is not limited to, presenting identification documents, biometric authentication, or two-factor authentication. For example, the identity verification unit can automatically verify the user's submitted identification documents. The identity verification unit can also perform identity verification using the user's biometric data. For example, the identity verification unit can perform identity verification using the user's fingerprints or facial recognition data. This enables the skill marketplace system according to the embodiment to efficiently input, analyze, provide, and verify the user's knowledge, skills, and experience.

[0066] The reception desk inputs the user's knowledge, skills, and experience. This includes, but is not limited to, technical skills, work experience, and specialized knowledge. The reception desk provides an interface for users to input their skills and experience in detail. Specifically, users can input information using text boxes and dropdown menus via a web browser or mobile application. This allows users to describe their skills and experience in detail and register them in the system. The reception desk can also store the user-entered information in a database. For example, it can store user-entered information in cloud storage and make it accessible as needed. Cloud storage incorporates encryption technology and backup functions to ensure data security and availability. Furthermore, the reception desk has the ability to automatically categorize and tag user-entered information. This allows subsequent analysis and delivery departments to process the data efficiently. For example, if a user enters skills such as "programming" or "project management," the reception desk categorizes these skills appropriately and assigns relevant tags. This streamlines data management across the entire system, enabling the provision of more appropriate services to users.

[0067] The analysis department analyzes the information entered by the reception department. Analysis is performed using methods such as data mining, statistical analysis, and machine learning algorithms, but is not limited to these examples. Specifically, the analysis department generates a plan to provide appropriate services based on the user's skills and experience. For example, it can use data mining techniques to extract common patterns and trends from the user's skill set and propose an optimal service plan. It can also use machine learning algorithms to compare the user's past data with data from other users to evaluate skills and experience. For example, the analysis department analyzes the user's skill set to determine which areas of service provision are optimal. Furthermore, the analysis department can evaluate skills and experience based on the user's input information. For example, the analysis department analyzes the user's skill set to determine which areas of service provision are optimal. AI-based analysis utilizes natural language processing techniques to analyze text data entered by the user and extract details of skills and experience. This allows the analysis department to accurately understand the user's skills and experience and generate an optimal service plan. Furthermore, the analysis department can provide advice on future career paths and skill development based on the user's skills and experience. For example, the analytics unit compares a user's current skill set with market demand and suggests which skills should be strengthened. This allows the analytics unit to provide users with more valuable information and maximize the effectiveness of the skill marketplace system.

[0068] The service provider delivers services based on information analyzed by the analysis provider. These services include, but are not limited to, consulting services, training programs, and technical support. Specifically, the service provider automatically executes the services requested by the user and provides the results. For example, if a user desires training on a specific technology, the service provider automatically schedules the training program and notifies the user. The service provider can also provide optimal services based on the user's skills and experience. For example, the service provider proposes a customized service plan based on the user's skill set, ensuring the user receives the service best suited to their needs. Furthermore, the service provider can monitor the service delivery status in real time and make adjustments as needed. For example, if a user encounters a problem during a training program, the service provider can respond immediately and provide appropriate support. The service provider can also collect user feedback and continuously improve the quality of its services. For example, it can collect user evaluations and opinions on the services received and revise the service content based on this feedback. This allows the service provider to continue providing high-quality services to users. Additionally, the service provider can integrate with other systems and platforms to provide users with a wider range of services. For example, by partnering with external experts and trainers, the service provider can offer users specialized consulting and training. This allows the service provider to meet the diverse needs of users and maximize the value of the skill marketplace system.

[0069] The Identity Verification Department verifies the user's identity to ensure the security of transactions. Identity verification is performed using methods such as, but is not limited to, presentation of identification documents, biometric authentication, and two-factor authentication. Specifically, the Identity Verification Department automatically verifies the identification documents submitted by the user. For example, if a user submits a driver's license or passport, the Identity Verification Department scans these documents and compares them against a database to verify the user's identity. The Identity Verification Department can also verify the user's identity using their biometric data. For example, it can use the user's fingerprints or facial recognition data to verify their identity. This allows users to complete identity verification quickly and reliably. Furthermore, the Identity Verification Department can enhance security by implementing two-factor authentication. For example, when a user logs in, they are required to enter a one-time passcode sent to their smartphone in addition to their password. This prevents unauthorized access and ensures the security of transactions. The Identity Verification Department also takes measures to securely manage user identity information and protect privacy. For example, identity information is encrypted and stored on a secure server. This minimizes the risk of unauthorized access to users' personal information. Furthermore, the identity verification department can conduct regular security audits to detect and improve system vulnerabilities. This allows the identity verification department to always maintain the latest security measures and ensure user safety.

[0070] The service provider can offer optimal services based on the user's skills and experience. For example, the service provider can analyze the user's skill set and propose the optimal service plan. For instance, the service provider can propose services such as website design and logo creation to a user with design skills. The service provider can also offer customized services based on the user's experience. For example, the service provider can offer business consulting services to a user with consulting experience. The service provider can also offer personalized services based on the user's needs. For example, the service provider can introduce a specialist with the optimal skill set according to the user's project. This improves the quality of service by providing optimal services based on the user's skills and experience. Some or all of the above processes in the service provider may be performed using AI, for example, or not. For example, the service provider can offer services using an AI model that takes the user's skill set as input and outputs the optimal service plan.

[0071] The identity verification unit can verify the user's identity. For example, the identity verification unit can automatically verify the user's submitted identification document. For example, the identity verification unit can analyze the image of the identification document to check for forgery. The identity verification unit can also verify the user's identity using biometric authentication. For example, the identity verification unit can verify the user's identity using the user's fingerprint or facial recognition data. The identity verification unit can also verify the user's identity using two-factor authentication. For example, the identity verification unit can verify the user's identity by combining the user's password with an authentication code sent to their smartphone. This ensures the security of transactions by verifying the user's identity. Some or all of the above processes in the identity verification unit may be performed using AI, for example, or without AI. For example, the identity verification unit can input the image data of the identification document into a generating AI and have the generating AI check for forgery.

[0072] The service provider can enhance the reliability of transactions through an evaluation system. For example, the service provider can collect user feedback and improve transaction reliability through the evaluation system. For example, the service provider can collect user evaluations of services provided and calculate a confidence score. The service provider can also enhance transaction reliability through performance evaluations. For example, the service provider can analyze a user's service provision history and perform a performance evaluation. The service provider can also determine a user's rank based on their confidence score. For example, the service provider can assign a high rank to users with high confidence scores. This improves transaction reliability through the evaluation system. Some or all of the above processes in the service provider may be performed using AI, for example, or not using AI. For example, the service provider can input user feedback data into a generating AI and have the generating AI calculate the confidence score.

[0073] The service provider can enhance the reliability of transactions through a ranking system. For example, the service provider can improve transaction reliability by assigning ranks based on user evaluation criteria. For example, the service provider can determine ranks based on users' service provision history and feedback. The service provider can also evaluate user reliability through the ranking process. For example, the service provider can assign a rank to users who meet certain evaluation criteria. The service provider can also continuously evaluate user reliability through the ranking system. For example, the service provider can periodically review user evaluations and update ranks. This improves transaction reliability through the ranking system. Some or all of the above processes in the service provider may be performed using AI, for example, or not using AI. For example, the service provider can input user evaluation data into a generating AI and have the generating AI determine the ranks.

[0074] The analysis unit can generate plans to provide appropriate services based on the user's skills and experience. For example, the analysis unit can analyze the user's skill set and generate an optimal service plan. For instance, it might propose service plans such as website design or logo creation to a user with design skills. The analysis unit can also generate customized service plans based on the user's experience. For example, it might generate a business consulting service plan for a user with consulting experience. The analysis unit can also generate personalized service plans based on the user's needs. For example, it might generate a plan to introduce experts with the most suitable skill sets for the user's project. This improves the quality of service by generating appropriate service plans based on the user's skills and experience. Some or all of the above-described processes in the analysis unit may be performed using, for example, generative AI, or without generative AI. For example, the analysis unit can generate plans using a generative AI model that takes the user's skill set as input and outputs an optimal service plan.

[0075] The service provider may include a competition section where users can compete in their skills to create AI agents. For example, the service provider could host a competition where users can create AI agents using their skills and experience. For example, the service provider could host a contest to compete in AI agent creation skills and award prizes to users who create outstanding AI agents. The service provider could also provide training programs for users to create AI agents. For example, the service provider could offer online courses to improve AI agent creation skills. The service provider could also provide resources for users to create AI agents. For example, the service provider could provide the tools and datasets necessary for creating AI agents. This would allow users to improve their skills by competing in AI agent creation. Some or all of the above processes in the service provider may be performed using, for example, generative AI, or not using generative AI. For example, the service provider could evaluate users' AI agent creation skills and determine the competition results based on generative AI.

[0076] The reception unit can estimate the user's emotions and adjust the timing of inputting knowledge, skills, and experience based on the estimated emotions. For example, if the user is stressed, the reception unit can delay the input timing to provide a relaxing environment. For example, if the user is relaxed, the reception unit can speed up the input timing to efficiently collect information. Also, if the user is in a hurry, the reception unit can optimize the input timing to quickly collect information. For example, the reception unit can dynamically adjust the input timing according to the user's emotional state. This allows for efficient information collection by adjusting the input timing 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 reception unit may be performed using a generative AI, or not. For example, the reception unit can input the user's emotion data into a generative AI and have the generative AI adjust the input timing.

[0077] The reception desk can analyze the user's past input history and select the optimal input method. For example, the reception desk can prioritize suggesting input methods (voice, text, etc.) that the user has frequently used in the past. For example, the reception desk can predict and suggest an input method to be used during a specific time period based on the user's past input history. The reception desk can also customize the optimal input method based on information the user has entered in the past. For example, the reception desk can analyze the user's past input data and suggest the optimal input method. In this way, the reception desk can suggest the optimal input method by analyzing past input history. Some or all of the above processing in the reception desk may be performed using AI, for example, or not using AI. For example, the reception desk can input the user's past input data into a generating AI and have the generating AI select the optimal input method.

[0078] The reception system can filter the input of knowledge, skills, and experience based on the user's current projects and areas of interest. For example, the reception system can prioritize inputting skills and experience related to the user's current project. For example, the reception system can filter and input relevant knowledge and skills based on the user's areas of interest. The reception system can also dynamically filter the necessary skills and experience according to the progress of the user's project. For example, the reception system can monitor the progress of the user's project in real time and suggest the necessary skills and experience. This allows for the input of highly relevant information by filtering based on the current project and areas of interest. Some or all of the above processing in the reception system may be performed using AI, for example, or not using AI. For example, the reception system can input the user's project data into a generating AI and have the generating AI perform the filtering.

[0079] The reception unit can estimate the user's emotions and determine the priority of the information to be entered based on the estimated emotions. For example, if the user is stressed, the reception unit will prioritize important information and postpone other information. For example, if the user is relaxed, the reception unit will prioritize detailed information. Also, if the user is in a hurry, the reception unit can quickly enter the most important information. For example, the reception unit can dynamically adjust the priority of information according to the user's emotional state. This enables efficient information entry by determining the priority of information 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 reception unit may be performed using a generative AI, or not using a generative AI. For example, the reception unit can input user emotion data into a generative AI and have the generative AI perform the determination of information priority.

[0080] The reception desk can prioritize inputting highly relevant information based on the user's geographical location when inputting knowledge, skills, and experience. For example, if the user is in a specific region, the reception desk will prioritize inputting skills and experience related to that region. For example, the reception desk can filter and input relevant knowledge and skills based on the user's current location. The reception desk can also suggest the optimal input method based on the user's geographical location. For example, the reception desk can acquire the user's geographical location in real time and suggest the optimal input method. This enables efficient information gathering by inputting highly relevant information based on geographical location. Some or all of the above processing in the reception desk may be performed using AI, for example, or without AI. For example, the reception desk can input the user's geographical location data into a generating AI and have the generating AI input highly relevant information.

[0081] The reception desk can analyze the user's social media activity and input relevant information when inputting knowledge, skills, and experience. For example, the reception desk can extract and input relevant skills and experience from the user's social media activity. For example, the reception desk can input relevant knowledge and skills based on the user's interests on social media. The reception desk can also analyze the content of the user's social media posts and suggest the optimal input method. For example, the reception desk can analyze the user's social media post data and suggest the optimal input method. This allows for efficient input of relevant information by analyzing social media activity. Some or all of the above processing in the reception desk may be performed using AI, for example, or without AI. For example, the reception desk can input the user's social media data into a generating AI and have the generating AI input the relevant information.

[0082] The analysis unit can estimate the user's emotions and adjust the presentation of the analysis based on the estimated emotions. For example, if the user is relaxed, the analysis unit provides detailed analysis results. For example, if the user is in a hurry, the analysis unit provides concise analysis results that get straight to the point. The analysis unit can also provide visually easy-to-understand analysis results if the user is stressed. For example, the analysis unit dynamically adjusts the presentation of the analysis according to the user's emotional state. This allows for easy-to-understand analysis results by adjusting the presentation of the analysis 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-described processes in the analysis unit may be performed using a generative AI, or not. For example, the analysis unit can input user emotion data into a generative AI and have the generative AI adjust the presentation of the analysis.

[0083] The analysis unit can adjust the level of detail of the analysis based on the importance of the information during the analysis. For example, the analysis unit can perform a detailed analysis on important information and simplify other information. For example, the analysis unit can adjust the depth of the analysis according to the importance of the information. The analysis unit can also prioritize the analysis of highly important information and provide detailed results. For example, the analysis unit can prioritize the analysis of information with high business impact and provide a detailed report. This allows for efficient analysis by adjusting the level of detail of the analysis based on the importance of the information. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input information importance data into a generating AI and have the generating AI perform the adjustment of the level of detail of the analysis.

[0084] The analysis unit can apply different analysis algorithms depending on the category of information during analysis. For example, the analysis unit can apply a design-specific analysis algorithm to design-related information. For example, the analysis unit can apply a consulting-specific analysis algorithm to consulting-related information. The analysis unit can also apply a writing-specific analysis algorithm to writing-related information. For example, the analysis unit can use a writing-specific algorithm to analyze information about users with writing skills. By applying analysis algorithms according to the category of information, highly accurate analysis becomes possible. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input information category data into a generating AI and have the generating AI select the analysis algorithm to apply.

[0085] The analysis unit can estimate the user's emotions and adjust the length of the analysis based on the estimated emotions. For example, if the user is in a hurry, the analysis unit provides a short, concise analysis result. For example, if the user is relaxed, the analysis unit provides a detailed analysis result. The analysis unit can also provide a visually easy-to-understand analysis result if the user is stressed. For example, the analysis unit dynamically adjusts the length of the analysis according to the user's emotional state. This allows for efficient analysis by adjusting the length of the analysis 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-described processes in the analysis unit may be performed using a generative AI, or not using a generative AI. For example, the analysis unit can input user emotion data into a generative AI and have the generative AI adjust the length of the analysis.

[0086] The analysis unit can determine the priority of analysis based on the information submission date during the analysis. For example, the analysis unit may prioritize the analysis of the most recent information and provide results quickly. For example, the analysis unit may postpone the analysis of older information. The analysis unit can also adjust the order of analysis based on the submission date. For example, the analysis unit may dynamically adjust the order of analysis based on the submission date. This enables efficient analysis by determining the priority of analysis based on the information submission date. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input information submission date data into a generating AI and have the generating AI perform the determination of analysis priority.

[0087] The analysis unit can adjust the order of analysis based on the relevance of the information during the analysis. For example, the analysis unit may prioritize the analysis of highly relevant information and provide results. For example, the analysis unit may postpone the analysis of less relevant information. The analysis unit can also dynamically adjust the order of analysis based on the relevance of the information. For example, the analysis unit can dynamically adjust the order of analysis based on the relevance of the information. This allows for efficient analysis by adjusting the order of analysis based on the relevance of the information. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input information relevance data into a generating AI and have the generating AI perform the adjustment of the analysis order.

[0088] The service provider can estimate the user's emotions and adjust the way the service is presented based on the estimated emotions. For example, if the user is relaxed, the service provider can provide a detailed service description. For example, if the user is in a hurry, the service provider can provide a concise service description that gets straight to the point. The service provider can also provide a visually easy-to-understand service description if the user is stressed. For example, the service provider can dynamically adjust the way the service is presented according to the user's emotional state. This makes it possible to provide an easy-to-understand service by adjusting the way the service is presented 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 service provider may be performed using a generative AI, or not using a generative AI. For example, the service provider can input user emotion data into a generative AI and have the generative AI adjust the way the service is presented.

[0089] The service provider can adjust the level of detail provided based on the importance of the service at the time of delivery. For example, the service provider can provide detailed explanations for important services and simplify other services. For example, the service provider can adjust the depth of the delivery according to the importance of the service. The service provider can also prioritize providing highly important services and provide detailed explanations. For example, the service provider can prioritize providing services with a high business impact and provide detailed explanations. This allows for efficient service delivery by adjusting the level of detail based on the importance of the service. Some or all of the above processing in the service provider may be performed using AI, for example, or not using AI. For example, the service provider can input service importance data into a generating AI and have the generating AI perform the adjustment of the level of detail of the delivery.

[0090] The service provider can apply different service provision algorithms depending on the service category at the time of provision. For example, the service provider can apply a design-specific service provision algorithm to design-related services. For example, the service provider can apply a consulting-specific service provision algorithm to consulting-related services. The service provider can also apply a writing-specific service provision algorithm to writing-related services. For example, the service provider can provide services to users with writing skills using a writing-specific algorithm. By applying service provision algorithms according to the service category, it becomes possible to provide highly accurate services. 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 service category data into a generating AI and have the generating AI select the service provision algorithm to apply.

[0091] The service provider can estimate the user's emotions and determine the priority of services to provide based on the estimated emotions. For example, if the user is stressed, the service provider will prioritize important services and postpone others. For example, if the user is relaxed, the service provider will prioritize detailed services. Also, if the user is in a hurry, the service provider can quickly provide the most important services. For example, the service provider can dynamically adjust service priorities according to the user's emotional state. This enables efficient service delivery by determining service priorities 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 service provider may be performed using a generative AI, or not using a generative AI. For example, the service provider can input user emotion data into a generative AI and have the generative AI perform the determination of service priorities.

[0092] The service provider can determine the priority of service delivery based on the submission date. For example, the service provider may prioritize the most recent services and provide results quickly. For example, the service provider may postpone the delivery of older services. The service provider can also adjust the order of delivery based on the submission date. For example, the service provider may dynamically adjust the order of delivery based on the submission date. This enables efficient service delivery by determining the priority of service delivery based on the submission date. Some or all of the above processing in the service provider may be performed using AI, for example, or not using AI. For example, the service provider can input service submission date data into a generating AI and have the generating AI perform the determination of the delivery priority.

[0093] The service delivery unit can adjust the order of service delivery based on the relevance of the services. For example, the service delivery unit may prioritize providing highly relevant services and deliver results. For example, the service delivery unit may postpone providing less relevant services. The service delivery unit can also dynamically adjust the order of service delivery based on the relevance of the services. For example, the service delivery unit dynamically adjusts the order of service delivery based on the relevance of the services. This enables efficient service delivery by adjusting the order of service delivery based on the relevance of the services. Some or all of the above processing in the service delivery unit may be performed using AI, for example, or without AI. For example, the service delivery unit can input service relevance data into a generating AI and have the generating AI perform the adjustment of the order of service delivery.

[0094] The identity verification unit can estimate the user's emotions and adjust the identity verification method based on the estimated emotions. For example, if the user is relaxed, the identity verification unit provides a detailed identity verification procedure. For example, if the user is in a hurry, the identity verification unit provides a concise identity verification procedure. Furthermore, if the user is stressed, the identity verification unit can also provide a visually easy-to-understand identity verification procedure. For example, the identity verification unit dynamically adjusts the identity verification method according to the user's emotional state. This enables efficient identity verification by adjusting the identity verification 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 identity verification unit may be performed using a generative AI, or not using a generative AI. For example, the identity verification unit can input user emotion data into a generative AI and have the generative AI perform the adjustment of the identity verification method.

[0095] The identity verification unit can select the most suitable verification method by referring to the user's past transaction history during identity verification. For example, if the user has traded frequently in the past, the identity verification unit can provide a simplified identity verification procedure. For example, the identity verification unit can select a highly reliable verification method from the user's past transaction history. The identity verification unit can also propose the most suitable identity verification procedure based on the user's transaction history. For example, the identity verification unit can analyze the user's transaction history and propose the most suitable verification method. This allows the unit to propose the most suitable identity verification method by referring to past transaction history. Some or all of the above processing in the identity verification unit may be performed using AI, for example, or without AI. For example, the identity verification unit can input the user's transaction history data into a generating AI and have the generating AI select the most suitable verification method.

[0096] The identity verification unit can customize the verification process based on the user's current situation during identity verification. For example, if the user is in a hurry, the identity verification unit can provide a quick verification procedure. For example, if the user is relaxed, the identity verification unit can provide a detailed verification procedure. The identity verification unit can also suggest the most suitable verification procedure based on the user's current situation. For example, the identity verification unit can suggest the most suitable verification method considering the user's current activities and environmental conditions. This allows for efficient identity verification by customizing the verification method based on the current situation. Some or all of the above-described processes in the identity verification unit may be performed using AI, for example, or not using AI. For example, the identity verification unit can input the user's current situation data into a generating AI and have the generating AI perform the customization of the verification method.

[0097] The identity verification unit can estimate the user's emotions and determine the priority of identity verification based on the estimated emotions. For example, if the user is stressed, the identity verification unit will prioritize important verification procedures and postpone other procedures. For example, if the user is relaxed, the identity verification unit will prioritize detailed verification procedures. Also, if the user is in a hurry, the identity verification unit can quickly perform the most important verification procedures. For example, the identity verification unit dynamically adjusts the priority of identity verification according to the user's emotional state. This enables efficient identity verification by determining the priority of identity verification 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 identity verification unit may be performed using a generative AI, or not using a generative AI. For example, the identity verification unit can input user emotion data into a generative AI and have the generative AI perform the determination of identity verification priorities.

[0098] The identity verification unit can select the most appropriate verification method based on the user's geographical location information during identity verification. For example, if the user is in a specific region, the identity verification unit will prioritize verification procedures related to that region. For example, the identity verification unit will filter and perform relevant verification procedures based on the user's current location. The identity verification unit can also propose the most appropriate verification method based on the user's geographical location information. For example, the identity verification unit can acquire the user's geographical location information in real time and propose the most appropriate verification method. This enables efficient identity verification by selecting the most appropriate verification method based on geographical location information. Some or all of the above-described processes in the identity verification unit may be performed using AI, for example, or without AI. For example, the identity verification unit can input the user's geographical location data into a generating AI and have the generating AI select the most appropriate verification method.

[0099] The identity verification unit can analyze a user's social media activity and propose verification methods during identity verification. For example, the identity verification unit can extract relevant verification procedures from the user's social media activity. For example, the identity verification unit can perform relevant verification procedures based on the user's interests on social media. The identity verification unit can also analyze the content of the user's social media posts and propose the most suitable verification method. For example, the identity verification unit can analyze the user's social media post data and propose the most suitable verification method. In this way, by analyzing social media activity, the optimal verification method can be proposed. Some or all of the above processing in the identity verification unit may be performed using AI, for example, or without AI. For example, the identity verification unit can input the user's social media data into a generating AI and have the generating AI execute the proposal of verification methods.

[0100] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0101] The reception desk can estimate the user's emotions and dynamically change the design of the input interface based on the estimated emotions. For example, if the user is stressed, the reception desk provides a simple interface with calming colors. If the user is relaxed, the reception desk provides a more colorful and interactive interface. If the user is in a hurry, the reception desk can also adjust the interface to highlight the most important information. This improves the user experience by adjusting the interface 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 reception desk may be performed using or without generative AI. For example, the reception desk can input user emotion data into a generative AI and have the generative AI perform the interface design changes.

[0102] The analysis unit can analyze a user's past trading history and select the optimal analysis algorithm. For example, the analysis unit can analyze patterns of successful past trades and apply the optimal analysis algorithm to new trades with similar patterns. The analysis unit can also suggest the optimal analysis method for specific skills and experience based on the user's past trading history. For example, the analysis unit can select an algorithm that performs a more detailed analysis on skills in which the user has received high ratings in the past. This improves the accuracy of the analysis by selecting the optimal analysis algorithm based on past trading history. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the user's trading history data into a generating AI and have the generating AI select the optimal analysis algorithm.

[0103] The service provider can estimate the user's emotions and customize the content of the services offered based on those emotions. For example, if the user is stressed, the service provider can suggest a relaxing service. If the user is relaxed, the service provider can suggest a challenging service. If the user is in a hurry, the service provider can also suggest a service that can be completed quickly. By customizing the service content according to the user's emotions, user satisfaction is improved. 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 service provider may be performed using a generative AI, or not. For example, the service provider can input user emotion data into a generative AI and have the generative AI perform the customization of the service content.

[0104] The identity verification unit can analyze a user's past identity verification history and select the most suitable verification method. For example, the identity verification unit can analyze the success rate of identity verification methods used by the user in the past and propose the method with the highest success rate. Furthermore, the identity verification unit can select the most suitable verification method in a specific situation based on the user's past identity verification history. For example, the identity verification unit can propose a verification method for a similar region based on the identity verification method the user has used in a specific region in the past. This improves the efficiency of identity verification by selecting the most suitable method based on past identity verification history. Some or all of the above-described processes in the identity verification unit may be performed using AI, for example, or without AI. For example, the identity verification unit can input the user's identity verification history data into a generating AI and have the generating AI select the most suitable verification method.

[0105] The service provider can estimate the user's emotions and adjust the timing of service delivery based on the estimated emotions. For example, if the user is stressed, the service provider can delay service delivery to allow time for relaxation. If the user is relaxed, the service provider can speed up service delivery for efficient service. If the user is in a hurry, the service provider can also deliver service quickly. This improves the user experience by adjusting the timing of service delivery 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 service provider may be performed using or without a generative AI. For example, the service provider can input user emotion data into a generative AI and have the generative AI adjust the timing of service delivery.

[0106] The analysis unit can suggest potential collaborations with other users based on the user's skills and experience. For example, the analysis unit can analyze the user's skill set and suggest other users with complementary skills. It can also analyze patterns of successful past collaborations based on the user's experience and suggest users with similar patterns. For example, the analysis unit can suggest collaboration partners suitable for similar projects based on data from successful past projects. This improves the project success rate by suggesting collaboration possibilities based on the user's skills and experience. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the user's skill set data into a generating AI and have the generating AI suggest collaboration partners.

[0107] The service provider can estimate the user's emotions and adjust the feedback method of the service based on the estimated user emotions. For example, if the user is stressed, the service provider will prioritize providing positive feedback. If the user is relaxed, the service provider will provide detailed feedback. If the user is in a hurry, the service provider may also provide concise feedback. By adjusting the feedback method according to the user's emotions, user satisfaction is improved. 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 service provider may be performed using a generative AI, or not using a generative AI. For example, the service provider can input user emotion data into a generative AI and have the generative AI adjust the feedback method.

[0108] The reception desk can analyze user input data in real time and check for consistency in the input content. For example, the reception desk can display a warning if the skills or experience entered by the user are inconsistent. The reception desk can also verify the consistency of the data entered by the user and suggest ways to supplement any missing information. For example, the reception desk can prompt the user to enter relevant experience and knowledge based on the skill set they entered. This ensures accurate information collection by checking the consistency of the input data. Some or all of the above processes in the reception desk may be performed using AI, for example, or not using AI. For example, the reception desk can input user input data into a generating AI and have the generating AI perform consistency checks.

[0109] The analysis unit can propose future career paths based on the user's skills and experience. For example, the analysis unit can analyze the user's skill set and propose skills that will be useful for their future career. Furthermore, based on the user's experience, the analysis unit can analyze patterns of successful career paths in the past and propose career paths with similar patterns. For example, based on data from projects the user has successfully completed in the past, the analysis unit can propose skills and experiences suitable for their future career. This improves the success rate of careers by proposing future career paths based on the user's skills and experience. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the user's skill set data into a generating AI and have the generating AI propose career paths.

[0110] The service provider can estimate the user's emotions and adjust the follow-up method of the service based on the estimated user emotions. For example, if the user is stressed, the service provider can reduce the frequency of follow-ups to avoid burdening the user. If the user is relaxed, the service provider can provide detailed follow-ups. Also, if the user is in a hurry, the service provider can provide rapid follow-ups. By adjusting the follow-up method according to the user's emotions, user satisfaction is improved. 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 service provider may be performed using a generative AI, or not using a generative AI. For example, the service provider can input user emotion data into a generative AI and have the generative AI adjust the follow-up method.

[0111] The following briefly describes the processing flow for example form 2.

[0112] Step 1: The reception desk inputs the user's knowledge, skills, and experience. This includes, for example, technical skills, work experience, and specialized knowledge. The reception desk provides an interface for users to input their skills and experience in detail, and the entered information can be saved to a database or cloud storage. Step 2: The analysis unit analyzes the information entered by the reception unit. The analysis is performed using methods such as data mining, statistical analysis, and machine learning algorithms. Based on the user's skills and experience, the analysis unit generates a plan to provide appropriate services and evaluates the user's skills and experience. Step 3: The service provider delivers services based on the information analyzed by the analysis provider. These services include consulting services, training programs, and technical support. The service provider automatically executes the services requested by the user and provides the results. It also proposes a customized service plan based on the user's skills and experience. Step 4: The Identity Verification Unit verifies the user's identity to ensure the security of the transaction. Identity verification is performed using methods such as presentation of identification documents, biometric authentication, and two-factor authentication. The Identity Verification Unit automatically verifies the identification documents submitted by the user and performs identity verification using biometric data.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] Each of the multiple elements described above, including the reception unit, analysis unit, service provision unit, and identity verification unit, is implemented by, for example, at least one of the smart device 14 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the smart device 14 and provides an interface for the user to input their skills and experience. The analysis unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and analyzes the user's input information to generate an appropriate service plan. The service provision unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and provides services based on the analyzed information. The identity verification unit is implemented by, for example, the control unit 46A of the smart device 14 and verifies the user's identity. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0117] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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).

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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.).

[0129] 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.

[0130] 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.

[0131] 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.

[0132] Each of the multiple elements described above, including the reception unit, analysis unit, service provision unit, and identity verification unit, is implemented, for example, in at least one of the smart glasses 214 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the smart glasses 214 and provides an interface for the user to input their skills and experience. The analysis unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12 and analyzes the user's input information to generate an appropriate service plan. The service provision unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12 and provides services based on the analyzed information. The identity verification unit is implemented, for example, by the control unit 46A of the smart glasses 214 and verifies the user's identity. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0133] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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).

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.).

[0145] 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.

[0146] 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.

[0147] 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.

[0148] Each of the multiple elements described above, including the reception unit, analysis unit, service provision unit, and identity verification unit, is implemented by, for example, at least one of the headset terminal 314 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the headset terminal 314 and provides an interface for the user to input their skills and experience. The analysis unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and analyzes the user's input information to generate an appropriate service plan. The service provision unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and provides services based on the analyzed information. The identity verification unit is implemented by, for example, the control unit 46A of the headset terminal 314 and verifies the user's identity. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0149] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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).

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.).

[0162] 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.

[0163] 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.

[0164] 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.

[0165] Each of the multiple elements described above, including the reception unit, analysis unit, service provision unit, and identity verification unit, is implemented by, for example, at least one of the robot 414 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the robot 414 and provides an interface for the user to input their skills and experience. The analysis unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and analyzes the user's input information to generate an appropriate service plan. The service provision unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and provides services based on the analyzed information. The identity verification unit is implemented by, for example, the control unit 46A of the robot 414 and verifies the user's identity. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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."

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] (Note 1) A reception desk where users input their knowledge, skills, and experience, An analysis unit analyzes the information input by the reception unit, A service provision unit provides services based on the information analyzed by the aforementioned analysis unit, It includes an identity verification section to ensure the security of transactions. A system characterized by the following features. (Note 2) The aforementioned supply unit is, We provide optimal services based on the user's skills and experience. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned identity verification unit is: Verify the user's identity. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned supply unit is, Enhance the reliability of transactions through the rating system. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned supply unit is, Enhancing the reliability of transactions through a ranking system. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned analysis unit, Generate a plan to provide appropriate services based on the user's skills and experience. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned supply unit is, It features a competition section where users can compete to showcase their skills in creating AI agents. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned reception unit is It estimates the user's emotions and adjusts the timing of knowledge, skills, and experience input based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned reception unit is Analyze the user's past input history and select the optimal input method. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned reception unit is When inputting knowledge, skills, and experience, filtering is performed based on the user's current projects and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned reception unit is It estimates the user's emotions and prioritizes the information to be entered based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned reception unit is When inputting knowledge, skills, and experience, the system prioritizes inputting highly relevant information based on the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned reception unit is When users input knowledge, skills, and experience, the system analyzes their social media activity and inputs relevant information. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit, The system estimates the user's emotions and adjusts the representation of the analysis based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit, During analysis, adjust the level of detail based on the importance of the information. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit, During analysis, different analysis algorithms are applied depending on the category of information. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit, It estimates the user's emotions and adjusts the length of the analysis based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned analysis unit, During the analysis, the priority of the analysis is determined based on when the information was submitted. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned analysis unit, During analysis, the order of analysis is adjusted based on the relevance of the information. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned supply unit is, We estimate the user's emotions and adjust how the service is presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned supply unit is, When providing a service, adjust the level of detail based on its importance. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned supply unit is, When providing a service, different delivery algorithms are applied depending on the service category. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned supply unit is, It estimates the user's emotions and determines the priority of the services to provide based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned supply unit is, When providing a service, we will prioritize its delivery based on when the service was submitted. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned supply unit is, When providing services, adjust the order of delivery based on the relevance of the services. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned identity verification unit is: The system estimates the user's emotions and adjusts the identity verification method based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned identity verification unit is: During identity verification, the system will refer to the user's past transaction history to select the most appropriate verification method. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned identity verification unit is: During identity verification, the verification method is customized based on the user's current status. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned identity verification unit is: The system estimates the user's emotions and determines the priority of identity verification based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned identity verification unit is: During identity verification, the system selects the most suitable verification method based on the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned identity verification unit is: During identity verification, we analyze the user's social media activity and propose verification methods. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0185] 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 reception desk where users input their knowledge, skills, and experience, An analysis unit analyzes the information input by the reception unit, A service provision unit provides services based on the information analyzed by the aforementioned analysis unit, It includes an identity verification section to ensure the security of transactions. A system characterized by the following features.

2. The aforementioned supply unit is, We provide optimal services based on the user's skills and experience. The system according to feature 1.

3. The aforementioned identity verification unit is: Verify the user's identity. The system according to feature 1.

4. The aforementioned supply unit is, Enhance the reliability of transactions through the rating system. The system according to feature 1.

5. The aforementioned supply unit is, Enhancing the reliability of transactions through a ranking system. The system according to feature 1.

6. The aforementioned analysis unit, Generate a plan to provide appropriate services based on the user's skills and experience. The system according to feature 1.

7. The aforementioned supply unit is, It includes a competition section where users can compete to showcase their skills in creating AI agents. The system according to feature 1.

8. The aforementioned reception unit is It estimates the user's emotions and adjusts the timing of knowledge, skills, and experience input based on the estimated user emotions. The system according to feature 1.

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