system

Abstract

The system according to this embodiment aims to efficiently match tasks between generating AI and humans. [Solution] The system according to the embodiment comprises a reception unit, a matching unit, a generation AI selection unit, and a human selection unit. The reception unit registers jobs. The matching unit matches the jobs registered by the reception unit with the generation AI and humans. The generation AI selection unit has the generation AI select a job. The human selection unit has humans select a job.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003] <000E017>

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, there is a problem that the role sharing between the generative AI and humans is not clear, and it is difficult to efficiently match the work.

[0005] The system according to the embodiment aims to efficiently match the work between the generative AI and humans.

Means for Solving the Problems

[0006] <00E0033>The system according to the embodiment includes a reception unit, a matching unit, a generative AI selection unit, and a human selection unit. The reception unit registers the work. The matching unit matches the work registered by the reception unit with the generative AI and humans. The generative AI selection unit allows the generative AI to select the work. The human selection unit allows a human to select the work. [Effects of the Invention]

[0007] The system according to this embodiment can efficiently match tasks between generating AI and humans. [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 signed communication interface (I / F) is an interface that includes a communication processor and an antenna. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[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. <0000********>

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

[0019] The smart device 14 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. Also, the reception device 38, the output device 40, and the camera 42 are 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 AI-powered job site platform according to an embodiment of the present invention is a system for solving jobs through the cooperation of a generative AI and a human. In this system, users register jobs they want to solve on the platform, and people who register generative AIs and people who register tasks that require physical effort compete for those jobs, thereby achieving optimal matching. For example, various jobs such as data analysis, report writing, and physical work are registered. This information is input into the platform's AI. Next, people who register generative AIs and people who register tasks that require physical effort compete for those jobs. The generative AI is responsible for intellectual tasks such as data analysis and report writing, while humans are responsible for physical work and tasks that the generative AI cannot handle. In this way, the generative AI and humans can cooperate to solve jobs. Furthermore, the platform's AI matches the registered jobs with the generative AIs and humans. For example, if a data analysis job is registered, the generative AI will be responsible for that job. On the other hand, if physical work is registered, a human will be responsible for that job. In this way, optimal matching is achieved. This enables the generative AI and humans to cooperate to solve jobs. Generative AI is evolving daily, making the impossible possible, and collaboration between generative AI and humans can lead to more efficient work solutions. Furthermore, considering the limitations of generative AI can enhance the value of essential workers. For example, having generative AI handle data analysis while humans perform physical tasks improves overall work efficiency. Humans can also compensate for the limitations of generative AI by handling tasks it cannot handle. In this way, generative AI and humans can collaborate to solve problems. This means that an AI-powered job search platform could leverage this collaborative approach between generative AI and humans to solve jobs.

[0029] The AI-powered job site platform according to this embodiment comprises a reception unit, a matching unit, a generation AI selection unit, and a human selection unit. The reception unit registers jobs that users want to solve. For example, various jobs such as data analysis, report creation, and physical work can be registered. The reception unit can register jobs, for example, by having users access the platform and input details of the jobs they want to solve. The reception unit can also use voice input or image recognition technology to allow users to easily register jobs. For example, a user can describe the job content by voice, and it can be converted into text data and registered. Alternatively, a user can upload an image, and the job content can be automatically extracted from the image and registered. The matching unit matches the jobs registered by the reception unit with generation AI and humans. For example, if a data analysis job is registered, the matching unit assigns that job to the generation AI. On the other hand, if physical work is registered, the matching unit assigns that job to a human. The matching unit can perform optimal matching based on criteria such as skill matching, interest matching, and experience matching. The generation AI selection unit has the generation AI select a job. For example, the Generative AI Selection Unit allows the Generative AI to automatically analyze the content of jobs and select jobs that are suitable for the user. The Generative AI Selection Unit can, for example, have the Generative AI select the optimal job based on past performance data and skill sets. The Generative AI Selection Unit can also have the Generative AI select the optimal job based on its current processing capabilities and state. The Human Selection Unit allows humans to select jobs. For example, the Human Selection Unit allows humans to select the optimal job based on their skills and interests. The Human Selection Unit can, for example, have humans select the optimal job based on their past experience and skill sets. The Human Selection Unit can also have humans select the optimal job based on their current state and circumstances. As a result, the AI ​​version platform of the job site according to this embodiment can efficiently handle everything from job registration to matching and selection.

[0030] The reception desk allows users to register tasks they wish to complete. These tasks can include various types of work, such as data analysis, report writing, and physical labor. Users can register tasks by accessing the platform and entering details. Specifically, they access the platform via a web browser or mobile app and fill out a dedicated form with information such as the job title, detailed description, required skills, deadline, and compensation. This allows the reception desk to accurately understand the job content based on the user's information and collect the necessary data for subsequent processing. The reception desk can also utilize voice input and image recognition technology to facilitate user registration. For example, a user can describe the job verbally, which is then converted into text for registration. Voice recognition technology uses natural language processing (NLP) to analyze the user's voice and convert it into accurate text. Furthermore, users can upload images, and the system can automatically extract and register the job content from those images. Image recognition technology uses machine learning algorithms to detect text and objects within images and automatically analyze the job content. This allows the reception desk to provide an environment where users can easily and quickly register tasks, improving the user experience.

[0031] The matching unit matches jobs registered by the reception unit with both a generation AI and a human. For example, if a data analysis job is registered, the matching unit assigns it to the generation AI. Conversely, if a physical task is registered, the matching unit assigns it to a human. Specifically, the matching unit analyzes the content of the registered job and uses criteria such as skill matching, interest matching, and experience matching to identify the most suitable resource for that job. Skill matching compares the skill set required for the job with the skill sets of the generation AI and the human to achieve the best match. Interest matching considers the past work history and areas of interest of the generation AI and the human to select a resource that matches the job content. Experience matching selects the most suitable resource to increase the success rate of the job based on past achievements and experience. Furthermore, the matching unit is designed to respond to real-time changes in circumstances. For example, it can dynamically select the most suitable resource by considering the current operational status and processing capacity of the generation AI and the human. This allows the matching unit to efficiently and effectively assign jobs to the generation AI and the human, improving overall work efficiency.

[0032] The Generative AI Selection Unit allows the Generative AI to select jobs. For example, the Generative AI Selection Unit allows the Generative AI to automatically analyze the job content and select a job that is suitable for it. Specifically, the Generative AI Selection Unit allows the Generative AI to select the optimal job based on past performance data and skill sets. The Generative AI uses machine learning algorithms to analyze data such as the success rate, processing time, and error rate of past jobs to identify the job that is best suited to it. The Generative AI Selection Unit can also allow the Generative AI to select the optimal job based on its current processing capacity and status. For example, the Generative AI monitors the current processing load and memory usage and selects an appropriate job to avoid overloading. Furthermore, the Generative AI Selection Unit is designed so that the Generative AI can improve its skills through self-learning. The Generative AI can acquire new skills by working on new jobs and use them to help with future job selections. As a result, the Generative AI Selection Unit allows the Generative AI to select jobs efficiently and effectively, improving overall work efficiency.

[0033] The Human Selection Unit allows users to choose their jobs. For example, it enables users to select the most suitable job based on their skills and interests. Specifically, the Human Selection Unit allows users to input their skill sets and areas of interest through a user interface, and then suggests the most suitable jobs based on that input. The user interface is designed to be intuitive and easy to use, making it easy for users to operate. For example, skills and areas of interest can be selected using dropdown menus and checkboxes. The Human Selection Unit also allows users to select the most suitable job based on their past experience and skill sets. For example, it suggests suitable jobs based on past successful projects and acquired qualifications. Furthermore, the Human Selection Unit can also allow users to select the most suitable job based on their current state and circumstances. For example, it can select jobs within a reasonable scope, taking into account the current workload and schedule. In this way, the Human Selection Unit enables users to select jobs efficiently and effectively, improving overall work efficiency.

[0034] The evaluation unit assesses the capabilities of the generative AI. For example, the evaluation unit conducts its assessment based on performance data such as the success rate, accuracy, and speed of tasks the generative AI has completed in the past. For instance, the evaluation unit assesses the accuracy and speed of the generative AI when completing data analysis tasks and evaluates its capabilities based on the results. The evaluation unit can also assess how well the generative AI can adapt to new tasks. For example, the evaluation unit assesses the performance of the generative AI when analyzing new datasets and evaluates its adaptability based on the results. Furthermore, the evaluation unit can assess the efficiency of the generative AI when processing multiple tasks simultaneously. For example, the evaluation unit assesses the performance of the generative AI when processing multiple data analysis tasks simultaneously and evaluates its efficiency based on the results. This allows the evaluation unit to thoroughly assess the capabilities of the generative AI and enable optimal job matching. Some or all of the above-described processes in the evaluation unit may be performed using AI, or not. For example, the evaluation unit can use an AI model that takes the generative AI's performance data as input to perform the evaluation.

[0035] The evaluation unit assesses human capabilities. For example, the evaluation unit performs evaluations based on performance data such as the success rate, accuracy, and speed of tasks previously completed by humans. For instance, the evaluation unit assesses the accuracy and speed at which humans complete data analysis tasks and evaluates human capabilities based on the results. The evaluation unit can also assess how well humans can adapt to new tasks. For example, the evaluation unit assesses a human's performance when analyzing a new dataset and evaluates their adaptability based on the results. Furthermore, the evaluation unit can assess a human's efficiency when handling multiple tasks simultaneously. For example, the evaluation unit assesses a human's performance when handling multiple data analysis tasks simultaneously and evaluates their efficiency based on the results. This allows the evaluation unit to assess human capabilities in detail and enable optimal job matching. Some or all of the above-described processes in the evaluation unit may be performed using AI, or not. For example, the evaluation unit can use an AI model that takes human performance data as input and evaluates human capabilities.

[0036] The monitoring unit monitors competition between generative AI and humans. For example, the monitoring unit monitors how generative AI and humans handle the same task and records the results. For example, the monitoring unit monitors the performance of generative AI and humans when they handle a data analysis task simultaneously and evaluates the fairness of the competition based on the results. The monitoring unit can also compare the performance of generative AI and humans when they handle different tasks. For example, the monitoring unit monitors the performance when the generative AI handles a data analysis task and humans handle physical work and evaluates the fairness of the competition based on the results. Furthermore, the monitoring unit can also monitor the performance of generative AI and humans when they work together on a task. For example, the monitoring unit monitors the effectiveness of their collaboration when the generative AI is responsible for data analysis and humans handle physical work and evaluates the fairness of the competition based on the results. In this way, the monitoring unit can closely monitor competition between generative AI and humans and ensure fair competition. Some or all of the above processing in the monitoring unit may be performed using AI, for example, or without AI. For example, the monitoring unit can perform monitoring using an AI model that takes performance data of generative AI and humans as input and evaluates the fairness of the competition.

[0037] The Facilitation Unit facilitates collaboration between generative AI and humans. For example, the Facilitation Unit evaluates the effectiveness of collaboration between generative AI and humans in processing tasks and proposes methods to promote collaboration based on the results. For example, the Facilitation Unit evaluates the effectiveness of collaboration when generative AI is responsible for data analysis and humans are responsible for physical work and proposes methods to promote collaboration based on the results. The Facilitation Unit can also evaluate the effectiveness of collaboration between generative AI and humans in processing the same task. For example, the Facilitation Unit evaluates the effectiveness of collaboration between generative AI and humans in processing a data analysis task together and proposes methods to promote collaboration based on the results. Furthermore, the Facilitation Unit can also evaluate the effectiveness of collaboration between generative AI and humans in processing different tasks. For example, the Facilitation Unit evaluates the effectiveness of collaboration when generative AI is responsible for data analysis and humans are responsible for report creation and proposes methods to promote collaboration based on the results. This allows the Facilitation Unit to evaluate collaboration between generative AI and humans in detail and improve overall work efficiency. Some or all of the above processing in the Facilitation Unit may be performed using AI, for example, or without AI. For example, the promotion unit can take collaborative data from generated AI and humans as input and propose methods to promote collaboration using an AI model that evaluates the effectiveness of the collaboration.

[0038] The matching unit evaluates the capabilities of both the generative AI and humans and performs the optimal matching. For example, the matching unit evaluates the performance data and skill sets of the generative AI, as well as the years of experience and skill sets of the humans. For instance, the matching unit evaluates the accuracy and speed at which the generative AI completes data analysis tasks and performs the optimal matching based on the results. The matching unit can also evaluate the success rate, accuracy, and speed of tasks previously completed by humans and perform the optimal matching based on the results. Furthermore, the matching unit can evaluate the current capabilities and status of both the generative AI and humans and perform the optimal matching based on the results. For example, the matching unit evaluates the current processing capacity and status of the generative AI and performs the optimal matching based on the results. The matching unit can also evaluate the current skill sets and health status of humans and perform the optimal matching based on the results. This allows the matching unit to evaluate the capabilities of both the generative AI and humans in detail and perform the optimal matching. Some or all of the above-described processes in the matching unit may be performed using AI, or not. For example, the matching unit can use an AI model that takes performance data from both the generative AI and humans as input and performs the matching.

[0039] The monitoring unit monitors competition between generative AI and humans to ensure fair competition. For example, the monitoring unit monitors how generative AI and humans handle the same task and records the results. For instance, it monitors the performance of generative AI and humans simultaneously handling a data analysis task and evaluates the fairness of the competition based on the results. The monitoring unit can also compare the performance of generative AI and humans when they handle different tasks. For example, it monitors the performance of generative AI when it handles a data analysis task and humans handle physical work and evaluates the fairness of the competition based on the results. Furthermore, the monitoring unit can monitor the performance of generative AI and humans when they collaborate to handle a task. For example, it monitors the effectiveness of collaboration when generative AI is responsible for data analysis and humans handle physical work and evaluates the fairness of the competition based on the results. This allows the monitoring unit to closely monitor competition between generative AI and humans and ensure fair competition. Some or all of the above-described processes in the monitoring unit may be performed using AI, for example, or without AI. For example, the monitoring unit can perform monitoring using an AI model that evaluates the fairness of competition, taking generated AI and human performance data as input.

[0040] The Facilitation Unit promotes collaboration between generative AI and humans, improving overall work efficiency. For example, the Facilitation Unit evaluates the effectiveness of collaboration between generative AI and humans in processing tasks and proposes methods to promote collaboration based on the results. For example, the Facilitation Unit evaluates the effectiveness of collaboration when generative AI is responsible for data analysis and humans are responsible for physical work and proposes methods to promote collaboration based on the results. The Facilitation Unit can also evaluate the effectiveness of collaboration between generative AI and humans in processing the same task. For example, the Facilitation Unit evaluates the effectiveness of collaboration when generative AI and humans jointly process a data analysis task and proposes methods to promote collaboration based on the results. Furthermore, the Facilitation Unit can also evaluate the effectiveness of collaboration between generative AI and humans in processing different tasks. For example, the Facilitation Unit evaluates the effectiveness of collaboration when generative AI is responsible for data analysis and humans are responsible for report creation and proposes methods to promote collaboration based on the results. In this way, the Facilitation Unit can evaluate collaboration between generative AI and humans in detail and improve overall work efficiency. Some or all of the above processing in the Facilitation Unit may be performed using AI, for example, or without AI. For example, the promotion unit can take collaborative data from generated AI and humans as input and propose methods to promote collaboration using an AI model that evaluates the effectiveness of the collaboration.

[0041] The reception desk can analyze a user's past registration history when they register a job and suggest the most suitable registration method. For example, the reception desk can automatically display as suggestions the types of jobs the user has frequently registered in the past. The reception desk can also prioritize suggesting registration methods (voice, text, etc.) that the user has used in the past. Furthermore, the reception desk can predict and suggest job registration trends for specific time periods based on the user's past registration history. For example, the reception desk can analyze the trends of jobs the user has registered in specific time periods in the past and suggest the most suitable jobs for that time period. In this way, the reception desk can suggest the most suitable registration method by analyzing the user's past registration history. Some or all of the above processes in the reception desk may be performed using AI, for example, or not. For example, the reception desk can input the user's past registration history data into a generating AI and have the generating AI suggest the most suitable registration method.

[0042] The reception desk can filter job registrations based on the user's current projects and areas of interest. For example, the reception desk can prioritize displaying jobs related to projects the user is currently working on. It can also suggest highly relevant jobs based on the user's areas of interest. Furthermore, the reception desk can analyze the user's past project history to suggest the most suitable jobs. For example, it can suggest jobs related to projects the user has been interested in in the past. This allows for the suggestion of highly relevant jobs by filtering based on the user's current projects and areas of interest. Some or all of the above processing in the reception desk may be performed using AI, or not. For example, the reception desk can input the user's project history data into a generating AI and have the generating AI suggest the most suitable jobs.

[0043] The reception desk can prioritize registering highly relevant jobs by considering the user's geographical location when they register a job. For example, the reception desk can prioritize suggesting jobs that are close to the user's current location. The reception desk can also analyze the user's past travel history and suggest highly relevant jobs. Furthermore, the reception desk can suggest the most suitable jobs based on the user's geographical location. For example, the reception desk can suggest jobs related to places the user has visited in the past. This allows for the priority registration of highly relevant jobs by considering the user's geographical location. Some or all of the above processes in the reception desk may be performed using AI, or not. For example, the reception desk can input the user's geographical location data into a generating AI and have the generating AI suggest the most suitable jobs.

[0044] The reception desk can analyze a user's social media activity when they register a job and register relevant jobs. For example, the reception desk can suggest relevant jobs based on the user's interests and preferences on social media. It can also analyze a user's social media activity history and suggest the most suitable jobs. Furthermore, the reception desk can suggest relevant jobs by referring to the activities of the user's followers and friends on social media. For example, the reception desk can suggest jobs related to topics that the user frequently mentions on social media. This allows for the registration of relevant jobs by analyzing the user's social media activity. Some or all of the above processes in the reception desk may be performed using AI, for example, or not. For example, the reception desk can input the user's social media data into a generating AI and have the generating AI suggest the most suitable jobs.

[0045] The matching unit can improve the accuracy of matching by considering the interrelationships between jobs during the matching process. For example, the matching unit can match multiple jobs to the same user if the job content is related. It can also match multiple jobs to the same user if the job skill sets overlap. Furthermore, the matching unit can perform optimal matching to avoid overlapping work hours. For example, the matching unit can analyze the content of jobs the user has completed in the past and suggest jobs related to that content. This improves the accuracy of matching by considering the interrelationships between jobs. Some or all of the above processes in the matching unit may be performed using AI, for example, or not. For example, the matching unit can input job content data into a generating AI and have the generating AI perform the optimal matching.

[0046] The matching unit can perform matching by considering the attribute information of job registrants. For example, the matching unit can perform optimal matching based on the job registrant's skill set. The matching unit can also perform optimal matching by referring to the job registrant's past performance data. Furthermore, the matching unit can perform optimal matching based on the job registrant's current projects and areas of interest. For example, the matching unit can analyze the content of jobs the job registrant has completed in the past and suggest jobs related to that content. This makes optimal matching possible by considering the attribute information of the job registrant. Some or all of the above processes in the matching unit may be performed using AI, for example, or not using AI. For example, the matching unit can input the job registrant's attribute information data into a generating AI and have the generating AI perform optimal matching.

[0047] The matching unit can perform matching while considering the geographical distribution of jobs. For example, if the job locations are close together, the matching unit can match multiple jobs to the same user. Furthermore, if the job locations are far apart, the matching unit can suggest the optimal travel route. In addition, the matching unit can perform optimal matching even when the job locations are different. For example, the matching unit can suggest jobs related to places the user has visited in the past. This allows for optimal matching by considering the geographical distribution of jobs. Some or all of the above processing in the matching unit may be performed using AI, or not. For example, the matching unit can input geographical distribution data of jobs into a generating AI and have the generating AI perform optimal matching.

[0048] The matching unit can improve the accuracy of matching by referring to relevant literature on the job during the matching process. For example, the matching unit can refer to literature related to the content of the job to perform the best possible match. It can also refer to literature related to the skill set of the job to perform the best possible match. Furthermore, the matching unit can refer to literature related to the time of day of the job to perform the best possible match. For example, the matching unit can analyze the content of jobs that the user has completed in the past and refer to literature related to that content to perform the best possible match. This improves the accuracy of matching by referring to relevant literature on the job. Some or all of the above processes in the matching unit may be performed using AI, for example, or not using AI. For example, the matching unit can input data on relevant literature on the job into a generating AI and have the generating AI perform the best possible match.

[0049] The generation AI selection unit can analyze the generation AI's past selection history and propose the optimal selection method. For example, the generation AI selection unit can automatically display as candidates the types of jobs that the generation AI has frequently selected in the past. The generation AI selection unit can also prioritize suggesting selection methods (voice, text, etc.) that the generation AI has used in the past. Furthermore, the generation AI selection unit can predict and suggest job selection trends for specific time periods based on the generation AI's past selection history. For example, the generation AI selection unit can analyze the trends of jobs that the generation AI has selected in specific time periods in the past and suggest the most suitable jobs for that time period. In this way, the optimal selection method can be suggested by analyzing the generation AI's past selection history. Some or all of the above processing in the generation AI selection unit may be performed using AI, for example, or without AI. For example, the generation AI selection unit can input the generation AI's past selection history data into the generation AI and have the generation AI propose the optimal selection method.

[0050] The generation AI selection unit can select jobs based on the current capabilities and state of the generation AI. For example, the generation AI selection unit can select the optimal job based on the current processing capacity of the generation AI. It can also select the optimal job based on the current state of the generation AI (such as battery level). Furthermore, the generation AI selection unit can select the optimal job based on the current task load of the generation AI. For example, the generation AI selection unit selects jobs within a range that does not exceed the current processing capacity of the generation AI. This allows for the selection of the optimal job based on the current capabilities and state of the generation AI. Some or all of the above-described processes in the generation AI selection unit may be performed using AI, for example, or without AI. For example, the generation AI selection unit can input data on the current capabilities and state of the generation AI into the generation AI and cause the generation AI to select the optimal job.

[0051] The AI ​​generation selection unit can prioritize selecting jobs that are highly relevant, taking into account the geographical location information of the jobs when selecting a job for the AI ​​generation unit. For example, the AI ​​generation selection unit can prioritize selecting jobs that are close to the AI ​​generation unit's current location. The AI ​​generation selection unit can also analyze the AI ​​generation unit's past travel history and select jobs that are highly relevant. Furthermore, the AI ​​generation selection unit can select the most suitable job based on the AI ​​generation unit's geographical location information. For example, the AI ​​generation selection unit can select jobs related to places the AI ​​generation unit has visited in the past. This allows for the priority selection of highly relevant jobs by considering the geographical location information of the jobs. Some or all of the above-described processes in the AI ​​generation selection unit may be performed using AI, for example, or without AI. For example, the AI ​​generation selection unit can input the geographical location information data of the AI ​​generation unit and have the AI ​​generation unit select the most suitable job.

[0052] The generation AI selection unit can improve the accuracy of its selections by referring to relevant literature when the generation AI makes its selections. For example, the generation AI selection unit can refer to literature related to the content of the work and make the optimal selection. It can also refer to literature related to the skill set of the work and make the optimal selection. Furthermore, it can refer to literature related to the time of day of the work and make the optimal selection. For example, the generation AI selection unit can analyze the content of work that the generation AI has completed in the past and refer to literature related to that content to make the optimal selection. This improves the accuracy of the selection by referring to relevant literature. Some or all of the above processing in the generation AI selection unit may be performed using AI, for example, or without using AI. For example, the generation AI selection unit can input relevant literature data for the work into the generation AI and have the generation AI make the optimal selection.

[0053] The human selection unit can analyze the user's past selection history and suggest the optimal selection method. For example, the human selection unit can automatically display as candidates the types of jobs the user has frequently selected in the past. The human selection unit can also prioritize suggesting selection methods (voice, text, etc.) that the user has used in the past. Furthermore, the human selection unit can predict and suggest job selection trends for specific time periods based on the user's past selection history. For example, the human selection unit can analyze the trends of jobs the user has selected in specific time periods in the past and suggest the most suitable jobs for that time period. In this way, the optimal selection method can be suggested by analyzing the user's past selection history. Some or all of the above processing in the human selection unit may be performed using AI, for example, or without AI. For example, the human selection unit can input the user's past selection history data into a generating AI and have the generating AI suggest the optimal selection method.

[0054] The human selection unit can select jobs based on the user's current abilities and status. For example, the human selection unit can select the most suitable job based on the user's current skill set. It can also select the most suitable job based on the user's current health status. Furthermore, the human selection unit can select the most suitable job based on the user's current project workload. For example, the human selection unit selects jobs that do not exceed the user's current skill set. This ensures that the most suitable job is selected based on the user's current abilities and status. Some or all of the above processes in the human selection unit may be performed using AI, for example, or without AI. For example, the human selection unit can input the user's current abilities and status data into a generating AI and have the generating AI perform the optimal job selection.

[0055] The human selection unit can prioritize selecting highly relevant jobs by considering the geographical location information of the jobs when the user makes a selection. For example, the human selection unit can prioritize selecting jobs that are close to the user's current location. The human selection unit can also analyze the user's past travel history and select highly relevant jobs. Furthermore, the human selection unit can select the most suitable job based on the user's geographical location information. For example, the human selection unit can select jobs related to places the user has visited in the past. In this way, by considering the geographical location information of the jobs, the human selection unit can prioritize selecting highly relevant jobs. Some or all of the above processing in the human selection unit may be performed using AI, for example, or not using AI. For example, the human selection unit can input the user's geographical location information data into a generating AI and have the generating AI perform the optimal job selection.

[0056] The human selection unit can improve the accuracy of its selections by referring to relevant literature when the user makes a selection. For example, the human selection unit can refer to literature related to the content of the work and make the optimal selection. It can also refer to literature related to the skill set of the work and make the optimal selection. Furthermore, it can refer to literature related to the time of day of the work and make the optimal selection. For example, the human selection unit can analyze the content of work that the user has completed in the past and refer to literature related to that content to make the optimal selection. This improves the accuracy of the selection by referring to relevant literature. Some or all of the above processing in the human selection unit may be performed using AI, for example, or not using AI. For example, the human selection unit can input data on relevant literature of the work into a generating AI and have the generating AI perform the optimal selection.

[0057] The evaluation unit can improve the accuracy of its evaluation by referring to the generation AI's past performance data during the evaluation process. For example, the evaluation unit can set optimal evaluation criteria based on the generation AI's past performance data. The evaluation unit can also improve the accuracy of its evaluation by referring to the generation AI's past successes. Furthermore, the evaluation unit can improve the accuracy of its evaluation by analyzing the generation AI's past failures. For example, the evaluation unit can analyze the content of work previously completed by the generation AI and perform an optimal evaluation by referring to performance data related to that content. This improves the accuracy of the evaluation by referring to the generation AI's past performance data. Some or all of the above processes in the evaluation unit may be performed using AI, for example, or without using AI. For example, the evaluation unit can input the generation AI's past performance data into the generation AI and have the generation AI perform an optimal evaluation.

[0058] The evaluation unit can perform an evaluation based on the current capabilities and state of the generating AI during the evaluation process. For example, the evaluation unit can perform an optimal evaluation based on the current processing capacity of the generating AI. The evaluation unit can also perform an optimal evaluation based on the current state of the generating AI (such as battery level). Furthermore, the evaluation unit can also perform an optimal evaluation based on the current task load of the generating AI. For example, the evaluation unit performs an evaluation within the limits that do not exceed the current processing capacity of the generating AI. This allows for an optimal evaluation based on the current capabilities and state of the generating AI. Some or all of the above-described processes in the evaluation unit may be performed using AI, for example, or without using AI. For example, the evaluation unit can input the current capabilities and state data of the generating AI into the generating AI and have the generating AI perform an optimal evaluation.

[0059] The evaluation unit can perform evaluations while considering the geographical distribution of the generated AI. For example, the evaluation unit can perform an optimal evaluation based on the current location of the generated AI. The evaluation unit can also perform an optimal evaluation by analyzing the past movement history of the generated AI. Furthermore, the evaluation unit can perform an optimal evaluation based on the geographical distribution of the generated AI. For example, the evaluation unit can evaluate the performance of tasks related to places the generated AI has visited in the past. This allows for an optimal evaluation by considering the geographical distribution of the generated AI. Some or all of the above processes in the evaluation unit may be performed using AI, for example, or without AI. For example, the evaluation unit can input geographical distribution data of the generated AI into the generated AI and have the generated AI perform an optimal evaluation.

[0060] The evaluation unit can improve the accuracy of its evaluation by referring to relevant literature on the generating AI during the evaluation process. For example, the evaluation unit can refer to literature related to the performance of the generating AI to perform an optimal evaluation. It can also refer to literature related to the skill set of the generating AI to perform an optimal evaluation. Furthermore, the evaluation unit can refer to literature related to the time of day of the generating AI to perform an optimal evaluation. For example, the evaluation unit can analyze the content of work that the generating AI has completed in the past and refer to literature related to that content to perform an optimal evaluation. This improves the accuracy of the evaluation by referring to relevant literature on the generating AI. Some or all of the above processes in the evaluation unit may be performed using AI, for example, or without AI. For example, the evaluation unit can input relevant literature data for the generating AI into the generating AI and have the generating AI perform an optimal evaluation.

[0061] The monitoring unit can improve the accuracy of monitoring by referring to past competition data between the generating AI and humans during monitoring. For example, the monitoring unit sets optimal monitoring criteria based on past competition data between the generating AI and humans. The monitoring unit can also improve the accuracy of monitoring by referring to past success stories between the generating AI and humans. Furthermore, the monitoring unit can improve the accuracy of monitoring by analyzing past failure stories between the generating AI and humans. For example, the monitoring unit analyzes the content of work previously completed by the generating AI and humans and performs optimal monitoring by referring to competition data related to that content. This improves the accuracy of monitoring by referring to past competition data between the generating AI and humans. Some or all of the above processes in the monitoring unit may be performed using AI, for example, or without AI. For example, the monitoring unit can input past competition data between the generating AI and humans into the generating AI and have the generating AI perform optimal monitoring.

[0062] The monitoring unit can perform monitoring based on the current capabilities and status of the generating AI and the human during monitoring. For example, the monitoring unit can perform optimal monitoring based on the current processing capabilities of the generating AI and the human. The monitoring unit can also perform optimal monitoring based on the current status of the generating AI and the human (such as battery level). Furthermore, the monitoring unit can also perform optimal monitoring based on the current task load of the generating AI and the human. For example, the monitoring unit performs monitoring within the limits that do not exceed the current processing capabilities of the generating AI and the human. This allows for optimal monitoring based on the current capabilities and status of the generating AI and the human. Some or all of the above processing in the monitoring unit may be performed using AI, for example, or without AI. For example, the monitoring unit can input data on the current capabilities and status of the generating AI and the human into the generating AI and cause the generating AI to perform optimal monitoring.

[0063] The monitoring unit can perform monitoring while considering the geographical distribution of the generating AI and humans. For example, the monitoring unit can perform optimal monitoring based on the current location of the generating AI and humans. The monitoring unit can also perform optimal monitoring by analyzing the past movement history of the generating AI and humans. Furthermore, the monitoring unit can perform optimal monitoring based on the geographical distribution of the generating AI and humans. For example, the monitoring unit can monitor the performance of tasks related to places that the generating AI and humans have visited in the past. This allows for optimal monitoring by considering the geographical distribution of the generating AI and humans. Some or all of the above processing in the monitoring unit may be performed using AI, for example, or without AI. For example, the monitoring unit can input geographical distribution data of the generating AI and humans into the generating AI and cause the generating AI to perform optimal monitoring.

[0064] The monitoring unit can improve the accuracy of monitoring by referring to relevant literature on the generation AI and humans during monitoring. For example, the monitoring unit can refer to literature related to the performance of the generation AI and humans to perform optimal monitoring. It can also refer to literature related to the skill sets of the generation AI and humans to perform optimal monitoring. Furthermore, the monitoring unit can refer to literature related to the time zones of the generation AI and humans to perform optimal monitoring. For example, the monitoring unit can analyze the content of work previously completed by the generation AI and humans and refer to relevant literature to perform optimal monitoring. This improves the accuracy of monitoring by referring to relevant literature on the generation AI and humans. Some or all of the above processing in the monitoring unit may be performed using AI, for example, or without AI. For example, the monitoring unit can input relevant literature data on the generation AI and humans into the generation AI and have the generation AI perform optimal monitoring.

[0065] The facilitation unit can improve the accuracy of collaboration by referring to past collaboration data between the generating AI and humans during collaboration promotion. For example, the facilitation unit sets the optimal collaboration promotion method based on past collaboration data between the generating AI and humans. The facilitation unit can also improve the accuracy of collaboration promotion by referring to past success stories between the generating AI and humans. Furthermore, the facilitation unit can improve the accuracy of collaboration promotion by analyzing past failure stories between the generating AI and humans. For example, the facilitation unit analyzes the content of work previously completed by the generating AI and humans and performs optimal collaboration promotion by referring to collaboration data related to that content. In this way, the accuracy of collaboration promotion is improved by referring to past collaboration data between the generating AI and humans. Some or all of the above processes in the facilitation unit may be performed using AI, for example, or without using AI. For example, the facilitation unit can input past collaboration data between the generating AI and humans into the generating AI and cause the generating AI to perform optimal collaboration promotion.

[0066] The facilitation unit can facilitate cooperation based on the current capabilities and status of the generating AI and the human during the process. For example, the facilitation unit can perform optimal cooperation based on the current processing capabilities of the generating AI and the human. The facilitation unit can also perform optimal cooperation based on the current status of the generating AI and the human (such as battery level). Furthermore, the facilitation unit can also perform optimal cooperation based on the current task load of the generating AI and the human. For example, the facilitation unit facilitates cooperation within the limits that do not exceed the current processing capabilities of the generating AI and the human. This allows for optimal cooperation based on the current capabilities and status of the generating AI and the human. Some or all of the above-described processes in the facilitation unit may be performed using AI, for example, or without AI. For example, the facilitation unit can input data on the current capabilities and status of the generating AI and the human into the generating AI and cause the generating AI to perform optimal cooperation.

[0067] The promotion unit can facilitate cooperation by considering the geographical distribution of the generating AI and humans. For example, the promotion unit can optimize cooperation based on the current locations of the generating AI and humans. The promotion unit can also analyze the past travel history of the generating AI and humans to optimize cooperation. Furthermore, the promotion unit can optimize cooperation based on the geographical distribution of the generating AI and humans. For example, the promotion unit can facilitate cooperation on tasks related to places that the generating AI and humans have visited in the past. This allows for optimal cooperation by considering the geographical distribution of the generating AI and humans. Some or all of the above processing in the promotion unit may be performed using AI, for example, or without AI. For example, the promotion unit can input geographical distribution data of the generating AI and humans into the generating AI and cause the generating AI to perform optimal cooperation.

[0068] The facilitation unit can improve the accuracy of collaboration by referring to relevant literature on both the generating AI and humans during collaboration. For example, the facilitation unit can refer to literature related to collaboration between the generating AI and humans to perform optimal collaboration. It can also refer to literature related to the skill sets of the generating AI and humans to perform optimal collaboration. Furthermore, the facilitation unit can refer to literature related to the time zones of the generating AI and humans to perform optimal collaboration. For example, the facilitation unit can analyze the content of work previously completed by the generating AI and humans and refer to relevant literature to perform optimal collaboration. This improves the accuracy of collaboration by referring to relevant literature on both the generating AI and humans. Some or all of the above processing in the facilitation unit may be performed using AI, for example, or without AI. For example, the facilitation unit can input relevant literature data on the generating AI and humans into the generating AI and have the generating AI perform optimal collaboration.

[0069] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0070] The reception desk can analyze a user's past registration history and suggest the most suitable way to register for jobs. For example, the reception desk can automatically display job types that the user has frequently registered for in the past as suggestions. It can also prioritize suggesting registration methods (voice, text, etc.) that the user has used in the past. Furthermore, the reception desk can predict and suggest job registration trends for specific time periods based on the user's past registration history. In this way, by analyzing a user's past registration history, the reception desk can suggest the most suitable registration method.

[0071] The matching system can improve matching accuracy by considering the interrelationships between jobs. For example, if the job content is related, it can match multiple jobs to the same user. It can also match multiple jobs to the same user if the job skill sets overlap. Furthermore, it can optimize matching to avoid overlapping work schedules. This improves matching accuracy by considering the interrelationships between jobs.

[0072] The AI ​​selection unit can analyze the AI's past selection history and suggest the optimal selection method. For example, it can automatically display the types of jobs the AI ​​has frequently selected in the past as candidates. It can also prioritize suggesting selection methods (voice, text, etc.) that the AI ​​has used in the past. Furthermore, it can predict and suggest job selection trends for specific time periods based on the AI's past selection history. In this way, by analyzing the AI's past selection history, it can suggest the optimal selection method.

[0073] The human selection unit can analyze a user's past selection history and suggest the optimal selection method. For example, it can automatically display job types that the user has frequently selected in the past as suggestions. It can also prioritize suggesting selection methods that the user has used in the past (voice, text, etc.). Furthermore, it can predict and suggest job selection trends for specific time periods based on the user's past selection history. In this way, by analyzing the user's past selection history, it can suggest the optimal selection method.

[0074] The evaluation unit can improve the accuracy of its evaluations by referring to the past performance data of the generating AI during the evaluation process. For example, it can set optimal evaluation criteria based on the past performance data of the generating AI. It can also improve the accuracy of its evaluations by referring to past success stories of the generating AI. Furthermore, it can improve the accuracy of its evaluations by analyzing past failure stories of the generating AI. In this way, the accuracy of the evaluation is improved by referring to the past performance data of the generating AI.

[0075] The monitoring unit can improve the accuracy of monitoring by referring to past competition data between the generating AI and humans during monitoring. For example, it can set optimal monitoring criteria based on past competition data between the generating AI and humans. It can also improve the accuracy of monitoring by referring to past success stories of the generating AI and humans. Furthermore, it can improve the accuracy of monitoring by analyzing past failure stories of the generating AI and humans. In this way, the accuracy of monitoring is improved by referring to past competition data between the generating AI and humans.

[0076] The following briefly describes the processing flow for example form 1.

[0077] Step 1: The reception desk registers the tasks that users want to complete. Various tasks can be registered, such as data analysis, report creation, and physical work. Users can register tasks by accessing the platform and entering the details of the task they want to complete. It is also possible to use voice input and image recognition technology to make it easier for users to register tasks. For example, a user can describe the task by voice, and that will be converted into text data and registered. Alternatively, a user can upload an image, and the system can automatically extract the task details from the image and register them. Step 2: The matching unit matches the jobs registered by the reception unit with the generation AI and humans. For example, if a data analysis job is registered, the matching unit assigns that job to the generation AI. On the other hand, if a physical task is registered, the matching unit assigns that job to a human. The matching unit can perform optimal matching based on criteria such as skill matching, interest matching, and experience matching. Step 3: The Generation AI Selection Unit allows the Generation AI to select a job. The Generation AI Selection Unit allows the Generation AI to automatically analyze the job content and select a job that suits it. The Generation AI Selection Unit allows the Generation AI to select the optimal job based on past performance data and skill sets. The Generation AI Selection Unit can also allow the Generation AI to select the optimal job based on its current processing capacity and status. Step 4: The Human Selection Unit allows humans to choose jobs. The Human Selection Unit allows humans to select the best job based on their skills and interests. The Human Selection Unit allows humans to select the best job based on their past experience and skill set. The Human Selection Unit can also allow humans to select the best job based on their current state and circumstances.

[0078] (Example of form 2) The AI-powered job site platform according to an embodiment of the present invention is a system for solving jobs through the cooperation of a generative AI and a human. In this system, users register jobs they want to solve on the platform, and people who register generative AIs and people who register tasks that require physical effort compete for those jobs, thereby achieving optimal matching. For example, various jobs such as data analysis, report writing, and physical work are registered. This information is input into the platform's AI. Next, people who register generative AIs and people who register tasks that require physical effort compete for those jobs. The generative AI is responsible for intellectual tasks such as data analysis and report writing, while humans are responsible for physical work and tasks that the generative AI cannot handle. In this way, the generative AI and humans can cooperate to solve jobs. Furthermore, the platform's AI matches the registered jobs with the generative AIs and humans. For example, if a data analysis job is registered, the generative AI will be responsible for that job. On the other hand, if physical work is registered, a human will be responsible for that job. In this way, optimal matching is achieved. This enables the generative AI and humans to cooperate to solve jobs. Generative AI is evolving daily, making the impossible possible, and collaboration between generative AI and humans can lead to more efficient work solutions. Furthermore, considering the limitations of generative AI can enhance the value of essential workers. For example, having generative AI handle data analysis while humans perform physical tasks improves overall work efficiency. Humans can also compensate for the limitations of generative AI by handling tasks it cannot handle. In this way, generative AI and humans can collaborate to solve problems. This means that an AI-powered job search platform could leverage this collaborative approach between generative AI and humans to solve jobs.

[0079] The AI-powered job site platform according to this embodiment comprises a reception unit, a matching unit, a generation AI selection unit, and a human selection unit. The reception unit registers jobs that users want to solve. For example, various jobs such as data analysis, report creation, and physical work can be registered. The reception unit can register jobs, for example, by having users access the platform and input details of the jobs they want to solve. The reception unit can also use voice input or image recognition technology to allow users to easily register jobs. For example, a user can describe the job content by voice, and it can be converted into text data and registered. Alternatively, a user can upload an image, and the job content can be automatically extracted from the image and registered. The matching unit matches the jobs registered by the reception unit with generation AI and humans. For example, if a data analysis job is registered, the matching unit assigns that job to the generation AI. On the other hand, if physical work is registered, the matching unit assigns that job to a human. The matching unit can perform optimal matching based on criteria such as skill matching, interest matching, and experience matching. The generation AI selection unit has the generation AI select a job. For example, the Generative AI Selection Unit allows the Generative AI to automatically analyze the content of jobs and select jobs that are suitable for the user. The Generative AI Selection Unit can, for example, have the Generative AI select the optimal job based on past performance data and skill sets. The Generative AI Selection Unit can also have the Generative AI select the optimal job based on its current processing capabilities and state. The Human Selection Unit allows humans to select jobs. For example, the Human Selection Unit allows humans to select the optimal job based on their skills and interests. The Human Selection Unit can, for example, have humans select the optimal job based on their past experience and skill sets. The Human Selection Unit can also have humans select the optimal job based on their current state and circumstances. As a result, the AI ​​version platform of the job site according to this embodiment can efficiently handle everything from job registration to matching and selection.

[0080] The reception desk allows users to register tasks they wish to complete. These tasks can include various types of work, such as data analysis, report writing, and physical labor. Users can register tasks by accessing the platform and entering details. Specifically, they access the platform via a web browser or mobile app and fill out a dedicated form with information such as the job title, detailed description, required skills, deadline, and compensation. This allows the reception desk to accurately understand the job content based on the user's information and collect the necessary data for subsequent processing. The reception desk can also utilize voice input and image recognition technology to facilitate user registration. For example, a user can describe the job verbally, which is then converted into text for registration. Voice recognition technology uses natural language processing (NLP) to analyze the user's voice and convert it into accurate text. Furthermore, users can upload images, and the system can automatically extract and register the job content from those images. Image recognition technology uses machine learning algorithms to detect text and objects within images and automatically analyze the job content. This allows the reception desk to provide an environment where users can easily and quickly register tasks, improving the user experience.

[0081] The matching unit matches jobs registered by the reception unit with both a generation AI and a human. For example, if a data analysis job is registered, the matching unit assigns it to the generation AI. Conversely, if a physical task is registered, the matching unit assigns it to a human. Specifically, the matching unit analyzes the content of the registered job and uses criteria such as skill matching, interest matching, and experience matching to identify the most suitable resource for that job. Skill matching compares the skill set required for the job with the skill sets of the generation AI and the human to achieve the best match. Interest matching considers the past work history and areas of interest of the generation AI and the human to select a resource that matches the job content. Experience matching selects the most suitable resource to increase the success rate of the job based on past achievements and experience. Furthermore, the matching unit is designed to respond to real-time changes in circumstances. For example, it can dynamically select the most suitable resource by considering the current operational status and processing capacity of the generation AI and the human. This allows the matching unit to efficiently and effectively assign jobs to the generation AI and the human, improving overall work efficiency.

[0082] The Generative AI Selection Unit allows the Generative AI to select jobs. For example, the Generative AI Selection Unit allows the Generative AI to automatically analyze the job content and select a job that is suitable for it. Specifically, the Generative AI Selection Unit allows the Generative AI to select the optimal job based on past performance data and skill sets. The Generative AI uses machine learning algorithms to analyze data such as the success rate, processing time, and error rate of past jobs to identify the job that is best suited to it. The Generative AI Selection Unit can also allow the Generative AI to select the optimal job based on its current processing capacity and status. For example, the Generative AI monitors the current processing load and memory usage and selects an appropriate job to avoid overloading. Furthermore, the Generative AI Selection Unit is designed so that the Generative AI can improve its skills through self-learning. The Generative AI can acquire new skills by working on new jobs and use them to help with future job selections. As a result, the Generative AI Selection Unit allows the Generative AI to select jobs efficiently and effectively, improving overall work efficiency.

[0083] The Human Selection Unit allows users to choose their jobs. For example, it enables users to select the most suitable job based on their skills and interests. Specifically, the Human Selection Unit allows users to input their skill sets and areas of interest through a user interface, and then suggests the most suitable jobs based on that input. The user interface is designed to be intuitive and easy to use, making it easy for users to operate. For example, skills and areas of interest can be selected using dropdown menus and checkboxes. The Human Selection Unit also allows users to select the most suitable job based on their past experience and skill sets. For example, it suggests suitable jobs based on past successful projects and acquired qualifications. Furthermore, the Human Selection Unit can also allow users to select the most suitable job based on their current state and circumstances. For example, it can select jobs within a reasonable scope, taking into account the current workload and schedule. In this way, the Human Selection Unit enables users to select jobs efficiently and effectively, improving overall work efficiency.

[0084] The evaluation unit assesses the capabilities of the generative AI. For example, the evaluation unit conducts its assessment based on performance data such as the success rate, accuracy, and speed of tasks the generative AI has completed in the past. For instance, the evaluation unit assesses the accuracy and speed of the generative AI when completing data analysis tasks and evaluates its capabilities based on the results. The evaluation unit can also assess how well the generative AI can adapt to new tasks. For example, the evaluation unit assesses the performance of the generative AI when analyzing new datasets and evaluates its adaptability based on the results. Furthermore, the evaluation unit can assess the efficiency of the generative AI when processing multiple tasks simultaneously. For example, the evaluation unit assesses the performance of the generative AI when processing multiple data analysis tasks simultaneously and evaluates its efficiency based on the results. This allows the evaluation unit to thoroughly assess the capabilities of the generative AI and enable optimal job matching. Some or all of the above-described processes in the evaluation unit may be performed using AI, or not. For example, the evaluation unit can use an AI model that takes the generative AI's performance data as input to perform the evaluation.

[0085] The evaluation unit assesses human capabilities. For example, the evaluation unit performs evaluations based on performance data such as the success rate, accuracy, and speed of tasks previously completed by humans. For instance, the evaluation unit assesses the accuracy and speed at which humans complete data analysis tasks and evaluates human capabilities based on the results. The evaluation unit can also assess how well humans can adapt to new tasks. For example, the evaluation unit assesses a human's performance when analyzing a new dataset and evaluates their adaptability based on the results. Furthermore, the evaluation unit can assess a human's efficiency when handling multiple tasks simultaneously. For example, the evaluation unit assesses a human's performance when handling multiple data analysis tasks simultaneously and evaluates their efficiency based on the results. This allows the evaluation unit to assess human capabilities in detail and enable optimal job matching. Some or all of the above-described processes in the evaluation unit may be performed using AI, or not. For example, the evaluation unit can use an AI model that takes human performance data as input and evaluates human capabilities.

[0086] The monitoring unit monitors competition between generative AI and humans. For example, the monitoring unit monitors how generative AI and humans handle the same task and records the results. For example, the monitoring unit monitors the performance of generative AI and humans when they handle a data analysis task simultaneously and evaluates the fairness of the competition based on the results. The monitoring unit can also compare the performance of generative AI and humans when they handle different tasks. For example, the monitoring unit monitors the performance when the generative AI handles a data analysis task and humans handle physical work and evaluates the fairness of the competition based on the results. Furthermore, the monitoring unit can also monitor the performance of generative AI and humans when they work together on a task. For example, the monitoring unit monitors the effectiveness of their collaboration when the generative AI is responsible for data analysis and humans handle physical work and evaluates the fairness of the competition based on the results. In this way, the monitoring unit can closely monitor competition between generative AI and humans and ensure fair competition. Some or all of the above processing in the monitoring unit may be performed using AI, for example, or without AI. For example, the monitoring unit can perform monitoring using an AI model that takes performance data of generative AI and humans as input and evaluates the fairness of the competition.

[0087] The Facilitation Unit facilitates collaboration between generative AI and humans. For example, the Facilitation Unit evaluates the effectiveness of collaboration between generative AI and humans in processing tasks and proposes methods to promote collaboration based on the results. For example, the Facilitation Unit evaluates the effectiveness of collaboration when generative AI is responsible for data analysis and humans are responsible for physical work and proposes methods to promote collaboration based on the results. The Facilitation Unit can also evaluate the effectiveness of collaboration between generative AI and humans in processing the same task. For example, the Facilitation Unit evaluates the effectiveness of collaboration between generative AI and humans in processing a data analysis task together and proposes methods to promote collaboration based on the results. Furthermore, the Facilitation Unit can also evaluate the effectiveness of collaboration between generative AI and humans in processing different tasks. For example, the Facilitation Unit evaluates the effectiveness of collaboration when generative AI is responsible for data analysis and humans are responsible for report creation and proposes methods to promote collaboration based on the results. This allows the Facilitation Unit to evaluate collaboration between generative AI and humans in detail and improve overall work efficiency. Some or all of the above processing in the Facilitation Unit may be performed using AI, for example, or without AI. For example, the promotion unit can take collaborative data from generated AI and humans as input and propose methods to promote collaboration using an AI model that evaluates the effectiveness of the collaboration.

[0088] The matching unit evaluates the capabilities of both the generative AI and humans and performs the optimal matching. For example, the matching unit evaluates the performance data and skill sets of the generative AI, as well as the years of experience and skill sets of the humans. For instance, the matching unit evaluates the accuracy and speed at which the generative AI completes data analysis tasks and performs the optimal matching based on the results. The matching unit can also evaluate the success rate, accuracy, and speed of tasks previously completed by humans and perform the optimal matching based on the results. Furthermore, the matching unit can evaluate the current capabilities and status of both the generative AI and humans and perform the optimal matching based on the results. For example, the matching unit evaluates the current processing capacity and status of the generative AI and performs the optimal matching based on the results. The matching unit can also evaluate the current skill sets and health status of humans and perform the optimal matching based on the results. This allows the matching unit to evaluate the capabilities of both the generative AI and humans in detail and perform the optimal matching. Some or all of the above-described processes in the matching unit may be performed using AI, or not. For example, the matching unit can use an AI model that takes performance data from both the generative AI and humans as input and performs the matching.

[0089] The monitoring unit monitors competition between generative AI and humans to ensure fair competition. For example, the monitoring unit monitors how generative AI and humans handle the same task and records the results. For instance, it monitors the performance of generative AI and humans simultaneously handling a data analysis task and evaluates the fairness of the competition based on the results. The monitoring unit can also compare the performance of generative AI and humans when they handle different tasks. For example, it monitors the performance of generative AI when it handles a data analysis task and humans handle physical work and evaluates the fairness of the competition based on the results. Furthermore, the monitoring unit can monitor the performance of generative AI and humans when they collaborate to handle a task. For example, it monitors the effectiveness of collaboration when generative AI is responsible for data analysis and humans handle physical work and evaluates the fairness of the competition based on the results. This allows the monitoring unit to closely monitor competition between generative AI and humans and ensure fair competition. Some or all of the above-described processes in the monitoring unit may be performed using AI, for example, or without AI. For example, the monitoring unit can perform monitoring using an AI model that evaluates the fairness of competition, taking generated AI and human performance data as input.

[0090] The Facilitation Unit promotes collaboration between generative AI and humans, improving overall work efficiency. For example, the Facilitation Unit evaluates the effectiveness of collaboration between generative AI and humans in processing tasks and proposes methods to promote collaboration based on the results. For example, the Facilitation Unit evaluates the effectiveness of collaboration when generative AI is responsible for data analysis and humans are responsible for physical work and proposes methods to promote collaboration based on the results. The Facilitation Unit can also evaluate the effectiveness of collaboration between generative AI and humans in processing the same task. For example, the Facilitation Unit evaluates the effectiveness of collaboration when generative AI and humans jointly process a data analysis task and proposes methods to promote collaboration based on the results. Furthermore, the Facilitation Unit can also evaluate the effectiveness of collaboration between generative AI and humans in processing different tasks. For example, the Facilitation Unit evaluates the effectiveness of collaboration when generative AI is responsible for data analysis and humans are responsible for report creation and proposes methods to promote collaboration based on the results. In this way, the Facilitation Unit can evaluate collaboration between generative AI and humans in detail and improve overall work efficiency. Some or all of the above processing in the Facilitation Unit may be performed using AI, for example, or without AI. For example, the promotion unit can take collaborative data from generated AI and humans as input and propose methods to promote collaboration using an AI model that evaluates the effectiveness of the collaboration.

[0091] The reception desk can estimate the user's emotions and adjust the job registration method based on the estimated emotions. For example, if the user is stressed, the reception desk can provide a simple interface and minimize the input steps. If the user is relaxed, the reception desk can also provide detailed input options and suggest a customizable input method. Furthermore, if the user is in a hurry, the reception desk can prioritize voice input to allow for quick job registration. For example, the reception desk can register the job by having the user describe the job verbally and converting it into text data. This allows for more appropriate job registration by adjusting the job registration method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception desk may be performed using AI or not. For example, the reception desk can input the user's voice data into a generative AI and have the generative AI perform the user's emotion estimation.

[0092] The reception desk can analyze a user's past registration history when they register a job and suggest the most suitable registration method. For example, the reception desk can automatically display as suggestions the types of jobs the user has frequently registered in the past. The reception desk can also prioritize suggesting registration methods (voice, text, etc.) that the user has used in the past. Furthermore, the reception desk can predict and suggest job registration trends for specific time periods based on the user's past registration history. For example, the reception desk can analyze the trends of jobs the user has registered in specific time periods in the past and suggest the most suitable jobs for that time period. In this way, the reception desk can suggest the most suitable registration method by analyzing the user's past registration history. Some or all of the above processes in the reception desk may be performed using AI, for example, or not. For example, the reception desk can input the user's past registration history data into a generating AI and have the generating AI suggest the most suitable registration method.

[0093] The reception desk can filter job registrations based on the user's current projects and areas of interest. For example, the reception desk can prioritize displaying jobs related to projects the user is currently working on. It can also suggest highly relevant jobs based on the user's areas of interest. Furthermore, the reception desk can analyze the user's past project history to suggest the most suitable jobs. For example, it can suggest jobs related to projects the user has been interested in in the past. This allows for the suggestion of highly relevant jobs by filtering based on the user's current projects and areas of interest. Some or all of the above processing in the reception desk may be performed using AI, or not. For example, the reception desk can input the user's project history data into a generating AI and have the generating AI suggest the most suitable jobs.

[0094] The reception desk can estimate the user's emotions and determine the priority of tasks to register based on the estimated emotions. For example, if the user is stressed, the reception desk will prioritize suggesting simple, quick tasks. Conversely, if the user is relaxed, the reception desk can suggest complex, time-consuming tasks. Furthermore, if the user is in a hurry, the reception desk can prioritize suggesting urgent tasks. For example, if the user is in a hurry, the reception desk will prioritize voice input to allow for quick task registration. This allows for more appropriate task registration by prioritizing tasks 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 reception desk may be performed using AI or not. For example, the reception desk can input the user's voice data into a generative AI and have the generative AI perform the user's emotion estimation.

[0095] The reception desk can prioritize registering highly relevant jobs by considering the user's geographical location when they register a job. For example, the reception desk can prioritize suggesting jobs that are close to the user's current location. The reception desk can also analyze the user's past travel history and suggest highly relevant jobs. Furthermore, the reception desk can suggest the most suitable jobs based on the user's geographical location. For example, the reception desk can suggest jobs related to places the user has visited in the past. This allows for the priority registration of highly relevant jobs by considering the user's geographical location. Some or all of the above processes in the reception desk may be performed using AI, or not. For example, the reception desk can input the user's geographical location data into a generating AI and have the generating AI suggest the most suitable jobs.

[0096] The reception desk can analyze a user's social media activity when they register a job and register relevant jobs. For example, the reception desk can suggest relevant jobs based on the user's interests and preferences on social media. It can also analyze a user's social media activity history and suggest the most suitable jobs. Furthermore, the reception desk can suggest relevant jobs by referring to the activities of the user's followers and friends on social media. For example, the reception desk can suggest jobs related to topics that the user frequently mentions on social media. This allows for the registration of relevant jobs by analyzing the user's social media activity. Some or all of the above processes in the reception desk may be performed using AI, for example, or not. For example, the reception desk can input the user's social media data into a generating AI and have the generating AI suggest the most suitable jobs.

[0097] The matching unit can estimate the user's emotions and adjust the matching criteria based on the estimated emotions. For example, if the user is stressed, the matching unit will prioritize matching them with simple, quick tasks. If the user is relaxed, the matching unit can also prioritize matching them with complex, time-consuming tasks. Furthermore, if the user is in a hurry, the matching unit can prioritize matching them with urgent tasks. For example, if the user is in a hurry, the matching unit will prioritize voice input to quickly match them with tasks. This allows for more appropriate matching by adjusting the matching criteria 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 may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the matching unit may be performed using AI or not. For example, the matching unit can input the user's voice data into the generative AI and have the generative AI perform the user's emotion estimation.

[0098] The matching unit can improve the accuracy of matching by considering the interrelationships between jobs during the matching process. For example, the matching unit can match multiple jobs to the same user if the job content is related. It can also match multiple jobs to the same user if the job skill sets overlap. Furthermore, the matching unit can perform optimal matching to avoid overlapping work hours. For example, the matching unit can analyze the content of jobs the user has completed in the past and suggest jobs related to that content. This improves the accuracy of matching by considering the interrelationships between jobs. Some or all of the above processes in the matching unit may be performed using AI, for example, or not. For example, the matching unit can input job content data into a generating AI and have the generating AI perform the optimal matching.

[0099] The matching unit can perform matching by considering the attribute information of job registrants. For example, the matching unit can perform optimal matching based on the job registrant's skill set. The matching unit can also perform optimal matching by referring to the job registrant's past performance data. Furthermore, the matching unit can perform optimal matching based on the job registrant's current projects and areas of interest. For example, the matching unit can analyze the content of jobs the job registrant has completed in the past and suggest jobs related to that content. This makes optimal matching possible by considering the attribute information of the job registrant. Some or all of the above processes in the matching unit may be performed using AI, for example, or not using AI. For example, the matching unit can input the job registrant's attribute information data into a generating AI and have the generating AI perform optimal matching.

[0100] The matching unit can estimate the user's emotions and adjust the order in which matching results are displayed based on the estimated emotions. For example, if the user is stressed, the matching unit may prioritize displaying simple, quick tasks. Conversely, if the user is relaxed, the matching unit may prioritize displaying complex, time-consuming tasks. Furthermore, if the user is in a hurry, the matching unit may prioritize urgent tasks. For instance, if the user is in a hurry, the matching unit may prioritize voice input to enable quick job matching. This allows for more appropriate matching results by adjusting the order in which matching results are displayed 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 may include, 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 matching unit may be performed using AI or not. For example, the matching unit can input the user's voice data into the generative AI and have the generative AI perform the user's emotion estimation.

[0101] The matching unit can perform matching while considering the geographical distribution of jobs. For example, if the job locations are close together, the matching unit can match multiple jobs to the same user. Furthermore, if the job locations are far apart, the matching unit can suggest the optimal travel route. In addition, the matching unit can perform optimal matching even when the job locations are different. For example, the matching unit can suggest jobs related to places the user has visited in the past. This allows for optimal matching by considering the geographical distribution of jobs. Some or all of the above processing in the matching unit may be performed using AI, or not. For example, the matching unit can input geographical distribution data of jobs into a generating AI and have the generating AI perform optimal matching.

[0102] The matching unit can improve the accuracy of matching by referring to relevant literature on the job during the matching process. For example, the matching unit can refer to literature related to the content of the job to perform the best possible match. It can also refer to literature related to the skill set of the job to perform the best possible match. Furthermore, the matching unit can refer to literature related to the time of day of the job to perform the best possible match. For example, the matching unit can analyze the content of jobs that the user has completed in the past and refer to literature related to that content to perform the best possible match. This improves the accuracy of matching by referring to relevant literature on the job. Some or all of the above processes in the matching unit may be performed using AI, for example, or not using AI. For example, the matching unit can input data on relevant literature on the job into a generating AI and have the generating AI perform the best possible match.

[0103] The Generative AI Selection Unit can estimate the emotions of the Generative AI and adjust its job selection method based on the estimated emotions of the Generative AI. For example, if the Generative AI is stressed, the Generative AI Selection Unit will prioritize selecting simple and quick tasks. Conversely, if the Generative AI is relaxed, the Generative AI Selection Unit can also prioritize selecting complex and time-consuming tasks. Furthermore, if the Generative AI is in a hurry, the Generative AI Selection Unit can prioritize urgent tasks. For example, if the Generative AI is in a hurry, the Generative AI Selection Unit will prioritize voice input to enable quick job selection. This allows for more appropriate job selection by adjusting the job selection method according to the emotions of the Generative AI. Emotion estimation is achieved using an emotion estimation function, for example, with an emotion engine or Generative AI. The 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 Generative AI Selection Unit may be performed using AI, for example, or without AI. For example, the generation AI selection unit can input internal state data of the generation AI into the generation AI and cause the generation AI to perform emotion estimation.

[0104] The generation AI selection unit can analyze the generation AI's past selection history and propose the optimal selection method. For example, the generation AI selection unit can automatically display as candidates the types of jobs that the generation AI has frequently selected in the past. The generation AI selection unit can also prioritize suggesting selection methods (voice, text, etc.) that the generation AI has used in the past. Furthermore, the generation AI selection unit can predict and suggest job selection trends for specific time periods based on the generation AI's past selection history. For example, the generation AI selection unit can analyze the trends of jobs that the generation AI has selected in specific time periods in the past and suggest the most suitable jobs for that time period. In this way, the optimal selection method can be suggested by analyzing the generation AI's past selection history. Some or all of the above processing in the generation AI selection unit may be performed using AI, for example, or without AI. For example, the generation AI selection unit can input the generation AI's past selection history data into the generation AI and have the generation AI propose the optimal selection method.

[0105] The generation AI selection unit can select jobs based on the current capabilities and state of the generation AI. For example, the generation AI selection unit can select the optimal job based on the current processing capacity of the generation AI. It can also select the optimal job based on the current state of the generation AI (such as battery level). Furthermore, the generation AI selection unit can select the optimal job based on the current task load of the generation AI. For example, the generation AI selection unit selects jobs within a range that does not exceed the current processing capacity of the generation AI. This allows for the selection of the optimal job based on the current capabilities and state of the generation AI. Some or all of the above-described processes in the generation AI selection unit may be performed using AI, for example, or without AI. For example, the generation AI selection unit can input data on the current capabilities and state of the generation AI into the generation AI and cause the generation AI to select the optimal job.

[0106] The Generative AI Selection Unit can estimate the emotions of the Generative AI and determine the priority of tasks to select based on the estimated emotions of the Generative AI. For example, if the Generative AI is stressed, the Generative AI Selection Unit will prioritize simple and quick tasks. Conversely, if the Generative AI is relaxed, the Generative AI Selection Unit can also prioritize complex and time-consuming tasks. Furthermore, if the Generative AI is in a hurry, the Generative AI Selection Unit can prioritize urgent tasks. For example, if the Generative AI is in a hurry, the Generative AI Selection Unit will prioritize voice input to enable quick task selection. This allows for more appropriate task selection by determining task priorities according to the emotions of the Generative AI. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or Generative AI. The 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 Generative AI Selection Unit may be performed using AI, for example, or without AI. For example, the generation AI selection unit can input internal state data of the generation AI into the generation AI and cause the generation AI to perform emotion estimation.

[0107] The AI ​​generation selection unit can prioritize selecting jobs that are highly relevant, taking into account the geographical location information of the jobs when selecting a job for the AI ​​generation unit. For example, the AI ​​generation selection unit can prioritize selecting jobs that are close to the AI ​​generation unit's current location. The AI ​​generation selection unit can also analyze the AI ​​generation unit's past travel history and select jobs that are highly relevant. Furthermore, the AI ​​generation selection unit can select the most suitable job based on the AI ​​generation unit's geographical location information. For example, the AI ​​generation selection unit can select jobs related to places the AI ​​generation unit has visited in the past. This allows for the priority selection of highly relevant jobs by considering the geographical location information of the jobs. Some or all of the above-described processes in the AI ​​generation selection unit may be performed using AI, for example, or without AI. For example, the AI ​​generation selection unit can input the geographical location information data of the AI ​​generation unit and have the AI ​​generation unit select the most suitable job.

[0108] The generation AI selection unit can improve the accuracy of its selections by referring to relevant literature when the generation AI makes its selections. For example, the generation AI selection unit can refer to literature related to the content of the work and make the optimal selection. It can also refer to literature related to the skill set of the work and make the optimal selection. Furthermore, it can refer to literature related to the time of day of the work and make the optimal selection. For example, the generation AI selection unit can analyze the content of work that the generation AI has completed in the past and refer to literature related to that content to make the optimal selection. This improves the accuracy of the selection by referring to relevant literature. Some or all of the above processing in the generation AI selection unit may be performed using AI, for example, or without using AI. For example, the generation AI selection unit can input relevant literature data for the work into the generation AI and have the generation AI make the optimal selection.

[0109] The human selection unit can estimate the user's emotions and adjust its job selection method based on the estimated emotions. For example, if the user is stressed, the human selection unit will prioritize easy, quick tasks. Conversely, if the user is relaxed, the human selection unit can prioritize complex, time-consuming tasks. Furthermore, if the user is in a hurry, the human selection unit can prioritize urgent tasks. For example, if the user is in a hurry, the human selection unit will prioritize voice input to allow for quick job selection. This allows for more appropriate job selection by adjusting the job selection method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the human selection unit may be performed using AI or not. For example, the human selection unit can input the user's voice data into a generative AI and have the generative AI perform the estimation of the user's emotions.

[0110] The human selection unit can analyze the user's past selection history and suggest the optimal selection method. For example, the human selection unit can automatically display as candidates the types of jobs the user has frequently selected in the past. The human selection unit can also prioritize suggesting selection methods (voice, text, etc.) that the user has used in the past. Furthermore, the human selection unit can predict and suggest job selection trends for specific time periods based on the user's past selection history. For example, the human selection unit can analyze the trends of jobs the user has selected in specific time periods in the past and suggest the most suitable jobs for that time period. In this way, the optimal selection method can be suggested by analyzing the user's past selection history. Some or all of the above processing in the human selection unit may be performed using AI, for example, or without AI. For example, the human selection unit can input the user's past selection history data into a generating AI and have the generating AI suggest the optimal selection method.

[0111] The human selection unit can select jobs based on the user's current abilities and status. For example, the human selection unit can select the most suitable job based on the user's current skill set. It can also select the most suitable job based on the user's current health status. Furthermore, the human selection unit can select the most suitable job based on the user's current project workload. For example, the human selection unit selects jobs that do not exceed the user's current skill set. This ensures that the most suitable job is selected based on the user's current abilities and status. Some or all of the above processes in the human selection unit may be performed using AI, for example, or without AI. For example, the human selection unit can input the user's current abilities and status data into a generating AI and have the generating AI perform the optimal job selection.

[0112] The human selection unit can estimate the user's emotions and determine the priority of tasks to select based on those emotions. For example, if the user is stressed, the human selection unit will prioritize simple, quick tasks. Conversely, if the user is relaxed, the human selection unit can prioritize complex, time-consuming tasks. Furthermore, if the user is in a hurry, the human selection unit can prioritize urgent tasks. For instance, if the user is in a hurry, the human selection unit might prioritize voice input to allow for quick task selection. This enables more appropriate task selection by prioritizing tasks 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 may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the human selection unit may be performed using AI or not. For example, the human selection unit can input the user's voice data into a generative AI and have the generative AI perform the user's emotion estimation.

[0113] The human selection unit can prioritize selecting highly relevant jobs by considering the geographical location information of the jobs when the user makes a selection. For example, the human selection unit can prioritize selecting jobs that are close to the user's current location. The human selection unit can also analyze the user's past travel history and select highly relevant jobs. Furthermore, the human selection unit can select the most suitable job based on the user's geographical location information. For example, the human selection unit can select jobs related to places the user has visited in the past. In this way, by considering the geographical location information of the jobs, the human selection unit can prioritize selecting highly relevant jobs. Some or all of the above processing in the human selection unit may be performed using AI, for example, or not using AI. For example, the human selection unit can input the user's geographical location information data into a generating AI and have the generating AI perform the optimal job selection.

[0114] The human selection unit can improve the accuracy of its selections by referring to relevant literature when the user makes a selection. For example, the human selection unit can refer to literature related to the content of the work and make the optimal selection. It can also refer to literature related to the skill set of the work and make the optimal selection. Furthermore, it can refer to literature related to the time of day of the work and make the optimal selection. For example, the human selection unit can analyze the content of work that the user has completed in the past and refer to literature related to that content to make the optimal selection. This improves the accuracy of the selection by referring to relevant literature. Some or all of the above processing in the human selection unit may be performed using AI, for example, or not using AI. For example, the human selection unit can input data on relevant literature of the work into a generating AI and have the generating AI perform the optimal selection.

[0115] The evaluation unit can estimate the emotions of the generating AI and adjust the evaluation criteria based on the estimated emotions of the generating AI. For example, if the generating AI is stressed, the evaluation unit will prioritize evaluating simple and quick tasks. Conversely, if the generating AI is relaxed, the evaluation unit can also prioritize evaluating complex and time-consuming tasks. Furthermore, if the generating AI is in a hurry, the evaluation unit can also prioritize evaluating urgent tasks. For example, if the generating AI is in a hurry, the evaluation unit can prioritize voice input to enable rapid evaluation. This allows for more appropriate evaluation by adjusting the evaluation criteria according to the emotions of the generating AI. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generating AI. The generating AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above processing in the evaluation unit may be performed using AI, for example, or not using AI. For example, the evaluation unit can input internal state data of the generating AI and have the generating AI perform emotion estimation.

[0116] The evaluation unit can improve the accuracy of its evaluation by referring to the generation AI's past performance data during the evaluation process. For example, the evaluation unit can set optimal evaluation criteria based on the generation AI's past performance data. The evaluation unit can also improve the accuracy of its evaluation by referring to the generation AI's past successes. Furthermore, the evaluation unit can improve the accuracy of its evaluation by analyzing the generation AI's past failures. For example, the evaluation unit can analyze the content of work previously completed by the generation AI and perform an optimal evaluation by referring to performance data related to that content. This improves the accuracy of the evaluation by referring to the generation AI's past performance data. Some or all of the above processes in the evaluation unit may be performed using AI, for example, or without using AI. For example, the evaluation unit can input the generation AI's past performance data into the generation AI and have the generation AI perform an optimal evaluation.

[0117] The evaluation unit can perform an evaluation based on the current capabilities and state of the generating AI during the evaluation process. For example, the evaluation unit can perform an optimal evaluation based on the current processing capacity of the generating AI. The evaluation unit can also perform an optimal evaluation based on the current state of the generating AI (such as battery level). Furthermore, the evaluation unit can also perform an optimal evaluation based on the current task load of the generating AI. For example, the evaluation unit performs an evaluation within the limits that do not exceed the current processing capacity of the generating AI. This allows for an optimal evaluation based on the current capabilities and state of the generating AI. Some or all of the above-described processes in the evaluation unit may be performed using AI, for example, or without using AI. For example, the evaluation unit can input the current capabilities and state data of the generating AI into the generating AI and have the generating AI perform an optimal evaluation.

[0118] The evaluation unit can estimate the emotions of the generating AI and adjust the display method of the evaluation results based on the estimated emotions of the generating AI. For example, if the generating AI is stressed, the evaluation unit can provide a simple and highly visible display method. The evaluation unit can also provide a display method that includes detailed information if the generating AI is relaxed. Furthermore, if the generating AI is in a hurry, the evaluation unit can provide a concise display method. For example, if the generating AI is in a hurry, the evaluation unit can prioritize voice input and display the evaluation results quickly. This allows for the provision of more appropriate evaluation results by adjusting the display method of the evaluation results according to the emotions of the generating AI. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generating AI. The generating AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the evaluation unit may be performed using AI, for example, or without AI. For example, the evaluation unit can input internal state data of the generating AI and cause the generating AI to perform emotion estimation.

[0119] The evaluation unit can perform evaluations while considering the geographical distribution of the generated AI. For example, the evaluation unit can perform an optimal evaluation based on the current location of the generated AI. The evaluation unit can also perform an optimal evaluation by analyzing the past movement history of the generated AI. Furthermore, the evaluation unit can perform an optimal evaluation based on the geographical distribution of the generated AI. For example, the evaluation unit can evaluate the performance of tasks related to places the generated AI has visited in the past. This allows for an optimal evaluation by considering the geographical distribution of the generated AI. Some or all of the above processes in the evaluation unit may be performed using AI, for example, or without AI. For example, the evaluation unit can input geographical distribution data of the generated AI into the generated AI and have the generated AI perform an optimal evaluation.

[0120] The evaluation unit can improve the accuracy of its evaluation by referring to relevant literature on the generating AI during the evaluation process. For example, the evaluation unit can refer to literature related to the performance of the generating AI to perform an optimal evaluation. It can also refer to literature related to the skill set of the generating AI to perform an optimal evaluation. Furthermore, the evaluation unit can refer to literature related to the time of day of the generating AI to perform an optimal evaluation. For example, the evaluation unit can analyze the content of work that the generating AI has completed in the past and refer to literature related to that content to perform an optimal evaluation. This improves the accuracy of the evaluation by referring to relevant literature on the generating AI. Some or all of the above processes in the evaluation unit may be performed using AI, for example, or without AI. For example, the evaluation unit can input relevant literature data for the generating AI into the generating AI and have the generating AI perform an optimal evaluation.

[0121] The monitoring unit can estimate the emotions of the generating AI and the human, and adjust the monitoring criteria based on the estimated emotions. For example, if the generating AI and the human are stressed, the monitoring unit will prioritize monitoring simple, quick tasks. Conversely, if the generating AI and the human are relaxed, the monitoring unit can also prioritize monitoring complex, time-consuming tasks. Furthermore, if the generating AI and the human are in a hurry, the monitoring unit can prioritize monitoring urgent tasks. For example, if the generating AI and the human are in a hurry, the monitoring unit will prioritize voice input to enable rapid monitoring. This allows for more appropriate monitoring by adjusting the monitoring criteria according to the emotions of the generating AI and the human. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generating AI. The generating 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 monitoring unit may be performed using AI, for example, or without AI. For example, the monitoring unit can input both the generative AI and human voice data into the generative AI, allowing the generative AI to perform emotion estimation.

[0122] The monitoring unit can improve the accuracy of monitoring by referring to past competition data between the generating AI and humans during monitoring. For example, the monitoring unit sets optimal monitoring criteria based on past competition data between the generating AI and humans. The monitoring unit can also improve the accuracy of monitoring by referring to past success stories between the generating AI and humans. Furthermore, the monitoring unit can improve the accuracy of monitoring by analyzing past failure stories between the generating AI and humans. For example, the monitoring unit analyzes the content of work previously completed by the generating AI and humans and performs optimal monitoring by referring to competition data related to that content. This improves the accuracy of monitoring by referring to past competition data between the generating AI and humans. Some or all of the above processes in the monitoring unit may be performed using AI, for example, or without AI. For example, the monitoring unit can input past competition data between the generating AI and humans into the generating AI and have the generating AI perform optimal monitoring.

[0123] The monitoring unit can perform monitoring based on the current capabilities and status of the generating AI and the human during monitoring. For example, the monitoring unit can perform optimal monitoring based on the current processing capabilities of the generating AI and the human. The monitoring unit can also perform optimal monitoring based on the current status of the generating AI and the human (such as battery level). Furthermore, the monitoring unit can also perform optimal monitoring based on the current task load of the generating AI and the human. For example, the monitoring unit performs monitoring within the limits that do not exceed the current processing capabilities of the generating AI and the human. This allows for optimal monitoring based on the current capabilities and status of the generating AI and the human. Some or all of the above processing in the monitoring unit may be performed using AI, for example, or without AI. For example, the monitoring unit can input data on the current capabilities and status of the generating AI and the human into the generating AI and cause the generating AI to perform optimal monitoring.

[0124] The monitoring unit can estimate the emotions of the generating AI and the human, and adjust the display method of the monitoring results based on the estimated emotions. For example, if the generating AI and the human are stressed, the monitoring unit can provide a simple and highly visible display method. The monitoring unit can also provide a display method that includes detailed information if the generating AI and the human are relaxed. Furthermore, if the generating AI and the human are in a hurry, the monitoring unit can provide a concise display method. For example, if the generating AI and the human are in a hurry, the monitoring unit can prioritize voice input and display the monitoring results quickly. This allows for the provision of more appropriate monitoring results by adjusting the display method of the monitoring results according to the emotions of the generating AI and the human. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generating AI. The generating 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 monitoring unit may be performed using AI, for example, or without AI. For example, the monitoring unit can input both the generative AI and human voice data into the generative AI, allowing the generative AI to perform emotion estimation.

[0125] The monitoring unit can perform monitoring while considering the geographical distribution of the generating AI and humans. For example, the monitoring unit can perform optimal monitoring based on the current location of the generating AI and humans. The monitoring unit can also perform optimal monitoring by analyzing the past movement history of the generating AI and humans. Furthermore, the monitoring unit can perform optimal monitoring based on the geographical distribution of the generating AI and humans. For example, the monitoring unit can monitor the performance of tasks related to places that the generating AI and humans have visited in the past. This allows for optimal monitoring by considering the geographical distribution of the generating AI and humans. Some or all of the above processing in the monitoring unit may be performed using AI, for example, or without AI. For example, the monitoring unit can input geographical distribution data of the generating AI and humans into the generating AI and cause the generating AI to perform optimal monitoring.

[0126] The monitoring unit can improve the accuracy of monitoring by referring to relevant literature on the generation AI and humans during monitoring. For example, the monitoring unit can refer to literature related to the performance of the generation AI and humans to perform optimal monitoring. It can also refer to literature related to the skill sets of the generation AI and humans to perform optimal monitoring. Furthermore, the monitoring unit can refer to literature related to the time zones of the generation AI and humans to perform optimal monitoring. For example, the monitoring unit can analyze the content of work previously completed by the generation AI and humans and refer to relevant literature to perform optimal monitoring. This improves the accuracy of monitoring by referring to relevant literature on the generation AI and humans. Some or all of the above processing in the monitoring unit may be performed using AI, for example, or without AI. For example, the monitoring unit can input relevant literature data on the generation AI and humans into the generation AI and have the generation AI perform optimal monitoring.

[0127] The facilitator can estimate the emotions of the generating AI and the human, and adjust the method of facilitating cooperation based on the estimated emotions. For example, if the generating AI and the human are stressed, the facilitator can prioritize cooperation on simple, quick tasks. Conversely, if the generating AI and the human are relaxed, the facilitator can also prioritize cooperation on complex, time-consuming tasks. Furthermore, if the generating AI and the human are in a hurry, the facilitator can prioritize cooperation on urgent tasks. For example, if the generating AI and the human are in a hurry, the facilitator can prioritize voice input to enable rapid cooperation. This allows for more appropriate cooperation by adjusting the method of facilitating cooperation according to the emotions of the generating AI and the human. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generating AI. The generating 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 facilitator may be performed using AI, for example, or without AI. For example, the acceleration unit can input both the generating AI and human voice data into the generating AI, allowing the generating AI to perform emotion estimation.

[0128] The facilitation unit can improve the accuracy of collaboration by referring to past collaboration data between the generating AI and humans during collaboration promotion. For example, the facilitation unit sets the optimal collaboration promotion method based on past collaboration data between the generating AI and humans. The facilitation unit can also improve the accuracy of collaboration promotion by referring to past success stories between the generating AI and humans. Furthermore, the facilitation unit can improve the accuracy of collaboration promotion by analyzing past failure stories between the generating AI and humans. For example, the facilitation unit analyzes the content of work previously completed by the generating AI and humans and performs optimal collaboration promotion by referring to collaboration data related to that content. In this way, the accuracy of collaboration promotion is improved by referring to past collaboration data between the generating AI and humans. Some or all of the above processes in the facilitation unit may be performed using AI, for example, or without using AI. For example, the facilitation unit can input past collaboration data between the generating AI and humans into the generating AI and cause the generating AI to perform optimal collaboration promotion.

[0129] The facilitation unit can facilitate cooperation based on the current capabilities and status of the generating AI and the human during the process. For example, the facilitation unit can perform optimal cooperation based on the current processing capabilities of the generating AI and the human. The facilitation unit can also perform optimal cooperation based on the current status of the generating AI and the human (such as battery level). Furthermore, the facilitation unit can also perform optimal cooperation based on the current task load of the generating AI and the human. For example, the facilitation unit facilitates cooperation within the limits that do not exceed the current processing capabilities of the generating AI and the human. This allows for optimal cooperation based on the current capabilities and status of the generating AI and the human. Some or all of the above-described processes in the facilitation unit may be performed using AI, for example, or without AI. For example, the facilitation unit can input data on the current capabilities and status of the generating AI and the human into the generating AI and cause the generating AI to perform optimal cooperation.

[0130] The facilitator can estimate the emotions of the generating AI and the human, and determine the priority of cooperation based on the estimated emotions. For example, if the generating AI and the human are stressed, the facilitator can prioritize cooperation on simple, quick tasks. Conversely, if the generating AI and the human are relaxed, the facilitator can also prioritize cooperation on complex, time-consuming tasks. Furthermore, if the generating AI and the human are in a hurry, the facilitator can prioritize cooperation on urgent tasks. For example, if the generating AI and the human are in a hurry, the facilitator can prioritize voice input to enable rapid cooperation. This allows for more appropriate cooperation by determining the priority of cooperation according to the emotions of the generating AI and the human. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generating AI. The generating 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 facilitator may be performed using AI, for example, or without AI. For example, the acceleration unit can input both the generating AI and human voice data into the generating AI, allowing the generating AI to perform emotion estimation.

[0131] The promotion unit can facilitate cooperation by considering the geographical distribution of the generating AI and humans. For example, the promotion unit can optimize cooperation based on the current locations of the generating AI and humans. The promotion unit can also analyze the past travel history of the generating AI and humans to optimize cooperation. Furthermore, the promotion unit can optimize cooperation based on the geographical distribution of the generating AI and humans. For example, the promotion unit can facilitate cooperation on tasks related to places that the generating AI and humans have visited in the past. This allows for optimal cooperation by considering the geographical distribution of the generating AI and humans. Some or all of the above processing in the promotion unit may be performed using AI, for example, or without AI. For example, the promotion unit can input geographical distribution data of the generating AI and humans into the generating AI and cause the generating AI to perform optimal cooperation.

[0132] The facilitation unit can improve the accuracy of collaboration by referring to relevant literature on both the generating AI and humans during collaboration. For example, the facilitation unit can refer to literature related to collaboration between the generating AI and humans to perform optimal collaboration. It can also refer to literature related to the skill sets of the generating AI and humans to perform optimal collaboration. Furthermore, the facilitation unit can refer to literature related to the time zones of the generating AI and humans to perform optimal collaboration. For example, the facilitation unit can analyze the content of work previously completed by the generating AI and humans and refer to relevant literature to perform optimal collaboration. This improves the accuracy of collaboration by referring to relevant literature on both the generating AI and humans. Some or all of the above processing in the facilitation unit may be performed using AI, for example, or without AI. For example, the facilitation unit can input relevant literature data on the generating AI and humans into the generating AI and have the generating AI perform optimal collaboration.

[0133] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0134] The reception desk can analyze a user's past registration history and suggest the most suitable way to register for jobs. For example, the reception desk can automatically display job types that the user has frequently registered for in the past as suggestions. It can also prioritize suggesting registration methods (voice, text, etc.) that the user has used in the past. Furthermore, the reception desk can predict and suggest job registration trends for specific time periods based on the user's past registration history. In this way, by analyzing a user's past registration history, the reception desk can suggest the most suitable registration method.

[0135] The evaluation unit can estimate the emotions of the generating AI and adjust the evaluation criteria based on the estimated emotions of the generating AI. For example, if the generating AI is stressed, it will prioritize evaluating simple tasks that can be completed quickly. Conversely, if the generating AI is relaxed, it can prioritize evaluating complex and time-consuming tasks. Furthermore, if the generating AI is in a hurry, it can prioritize evaluating urgent tasks. In this way, by adjusting the evaluation criteria according to the emotions of the generating AI, more appropriate evaluations become possible.

[0136] The monitoring unit can estimate the emotions of the generating AI and humans, and adjust the monitoring criteria based on these estimated emotions. For example, if the generating AI and humans are stressed, it will prioritize monitoring simple, quick tasks. Conversely, if the generating AI and humans are relaxed, it can prioritize monitoring complex, time-consuming tasks. Furthermore, if the generating AI and humans are in a hurry, it can prioritize monitoring urgent tasks. This allows for more appropriate monitoring by adjusting the monitoring criteria according to the emotions of the generating AI and humans.

[0137] The cooperation unit can estimate the emotions of the generating AI and the human, and adjust the method of cooperation based on the estimated emotions. For example, if the generating AI and the human are feeling stressed, it can prioritize cooperation on simple tasks that can be completed quickly. Conversely, if the generating AI and the human are relaxed, it can prioritize cooperation on complex and time-consuming tasks. Furthermore, if the generating AI and the human are in a hurry, it can prioritize cooperation on urgent tasks. In this way, by adjusting the method of cooperation according to the emotions of the generating AI and the human, more appropriate cooperation becomes possible.

[0138] The reception desk can estimate the user's emotions and adjust the job registration process based on those emotions. For example, if the user is stressed, it can provide a simple interface and minimize the input steps. If the user is relaxed, it can provide detailed input options and suggest customizable input methods. Furthermore, if the user is in a hurry, it can prioritize voice input to allow for quick job registration. By adjusting the job registration process according to the user's emotions, it enables more appropriate job registration.

[0139] The matching system can improve matching accuracy by considering the interrelationships between jobs. For example, if the job content is related, it can match multiple jobs to the same user. It can also match multiple jobs to the same user if the job skill sets overlap. Furthermore, it can optimize matching to avoid overlapping work schedules. This improves matching accuracy by considering the interrelationships between jobs.

[0140] The AI ​​selection unit can analyze the AI's past selection history and suggest the optimal selection method. For example, it can automatically display the types of jobs the AI ​​has frequently selected in the past as candidates. It can also prioritize suggesting selection methods (voice, text, etc.) that the AI ​​has used in the past. Furthermore, it can predict and suggest job selection trends for specific time periods based on the AI's past selection history. In this way, by analyzing the AI's past selection history, it can suggest the optimal selection method.

[0141] The human selection unit can analyze a user's past selection history and suggest the optimal selection method. For example, it can automatically display job types that the user has frequently selected in the past as suggestions. It can also prioritize suggesting selection methods that the user has used in the past (voice, text, etc.). Furthermore, it can predict and suggest job selection trends for specific time periods based on the user's past selection history. In this way, by analyzing the user's past selection history, it can suggest the optimal selection method.

[0142] The evaluation unit can improve the accuracy of its evaluations by referring to the past performance data of the generating AI during the evaluation process. For example, it can set optimal evaluation criteria based on the past performance data of the generating AI. It can also improve the accuracy of its evaluations by referring to past success stories of the generating AI. Furthermore, it can improve the accuracy of its evaluations by analyzing past failure stories of the generating AI. In this way, the accuracy of the evaluation is improved by referring to the past performance data of the generating AI.

[0143] The monitoring unit can improve the accuracy of monitoring by referring to past competition data between the generating AI and humans during monitoring. For example, it can set optimal monitoring criteria based on past competition data between the generating AI and humans. It can also improve the accuracy of monitoring by referring to past success stories of the generating AI and humans. Furthermore, it can improve the accuracy of monitoring by analyzing past failure stories of the generating AI and humans. In this way, the accuracy of monitoring is improved by referring to past competition data between the generating AI and humans.

[0144] The following briefly describes the processing flow for example form 2.

[0145] Step 1: The reception desk registers the tasks that users want to complete. Various tasks can be registered, such as data analysis, report creation, and physical work. Users can register tasks by accessing the platform and entering the details of the task they want to complete. It is also possible to use voice input and image recognition technology to make it easier for users to register tasks. For example, a user can describe the task by voice, and that will be converted into text data and registered. Alternatively, a user can upload an image, and the system can automatically extract the task details from the image and register them. Step 2: The matching unit matches the jobs registered by the reception unit with the generation AI and humans. For example, if a data analysis job is registered, the matching unit assigns that job to the generation AI. On the other hand, if a physical task is registered, the matching unit assigns that job to a human. The matching unit can perform optimal matching based on criteria such as skill matching, interest matching, and experience matching. Step 3: The Generation AI Selection Unit allows the Generation AI to select a job. The Generation AI Selection Unit allows the Generation AI to automatically analyze the job content and select a job that suits it. The Generation AI Selection Unit allows the Generation AI to select the optimal job based on past performance data and skill sets. The Generation AI Selection Unit can also allow the Generation AI to select the optimal job based on its current processing capacity and status. Step 4: The Human Selection Unit allows humans to choose jobs. The Human Selection Unit allows humans to select the best job based on their skills and interests. The Human Selection Unit allows humans to select the best job based on their past experience and skill set. The Human Selection Unit can also allow humans to select the best job based on their current state and circumstances.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] Each of the multiple elements described above, including the reception unit, matching unit, generation AI selection unit, human selection unit, evaluation unit, monitoring unit, and promotion unit, is implemented, for example, in 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, where the user registers the work they want to solve. The matching unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, where the registered work is matched between the generation AI and a human. The generation AI selection unit and the human selection unit are implemented, for example, by the control unit 46A of the smart device 14 or the specific processing unit 290 of the data processing unit 12. The evaluation unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, where the capabilities of the generation AI and the human are evaluated. The monitoring unit is implemented, for example, by the control unit 46A of the smart device 14 or the specific processing unit 290 of the data processing unit 12, where the competition between the generation AI and the human is monitored. The promotion unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, where cooperation between the generation AI and the human is promoted. The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0150] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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).

[0156] 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.

[0157] 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.

[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 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.

[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 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.

[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 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.

[0165] Each of the multiple elements described above, including the reception unit, matching unit, generation AI selection unit, human selection unit, evaluation unit, monitoring unit, and promotion 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, where the user registers the task they want to solve. The matching unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, where the registered task is matched between the generation AI and a human. The generation AI selection unit and the human selection unit are implemented, for example, by the control unit 46A of the smart glasses 214 or the specific processing unit 290 of the data processing unit 12. The evaluation unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, where the capabilities of the generation AI and the human are evaluated. The monitoring unit is implemented, for example, by the control unit 46A of the smart glasses 214 or the specific processing unit 290 of the data processing unit 12, where the competition between the generation AI and the human is monitored. The promotion unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, where cooperation between the generation AI and the human is promoted. The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0166] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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).

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.).

[0178] 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.

[0179] 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.

[0180] 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.

[0181] Each of the multiple elements described above, including the reception unit, matching unit, generation AI selection unit, human selection unit, evaluation unit, monitoring unit, and promotion 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, where the user registers the job they want to solve. The matching unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, where the registered job is matched between the generation AI and a human. The generation AI selection unit and the human selection unit are implemented by, for example, the control unit 46A of the headset terminal 314 or the specific processing unit 290 of the data processing unit 12. The evaluation unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, where the capabilities of the generation AI and the human are evaluated. The monitoring unit is implemented by, for example, the control unit 46A of the headset terminal 314 or the specific processing unit 290 of the data processing unit 12, where the competition between the generation AI and the human is monitored. The acceleration unit is implemented, for example, by the specific processing unit 290 of the data processing device 12, and facilitates cooperation between the generating AI and humans. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0182] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0183] 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.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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).

[0188] 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.

[0189] 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.

[0190] 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.

[0191] 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.

[0192] 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.

[0193] 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.

[0194] 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.).

[0195] 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.

[0196] 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.

[0197] 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.

[0198] Each of the multiple elements described above, including the reception unit, matching unit, generation AI selection unit, human selection unit, evaluation unit, monitoring unit, and promotion unit, is implemented, for example, by 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, where the user registers the work they want to solve. The matching unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, where the registered work is matched between the generation AI and a human. The generation AI selection unit and the human selection unit are implemented, for example, by the control unit 46A of the robot 414 or the specific processing unit 290 of the data processing unit 12. The evaluation unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, where the capabilities of the generation AI and the human are evaluated. The monitoring unit is implemented, for example, by the control unit 46A of the robot 414 or the specific processing unit 290 of the data processing unit 12, where the competition between the generation AI and the human is monitored. The promotion unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, where cooperation between the generation AI and the human is promoted. The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0199] 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.

[0200] 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.

[0201] 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.

[0202] 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.

[0203] 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.

[0204] 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."

[0205] 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.

[0206] 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.

[0207] 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.

[0208] 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.

[0209] 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.

[0210] 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.

[0211] 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.

[0212] 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.

[0213] 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.

[0214] 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.

[0215] 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.

[0216] 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.

[0217] (Note 1) The reception desk where you register for work, A matching unit that matches the jobs registered by the aforementioned reception unit with a generating AI and a human, The generation AI selection unit selects a job, It includes a human selection unit where humans choose jobs. A system characterized by the following features. (Note 2) It includes an evaluation unit to assess the capabilities of the generating AI. The system described in Appendix 1, characterized by the features described herein. (Note 3) It has an evaluation unit that assesses human capabilities. The system described in Appendix 1, characterized by the features described herein. (Note 4) It includes a monitoring unit that oversees the competition between generating AI and humans. The system described in Appendix 1, characterized by the features described herein. (Note 5) It features a facilitator that promotes collaboration between generating AI and humans. The system described in Appendix 1, characterized by the features described herein. (Note 6) The matching unit is Evaluate the capabilities of both the generating AI and humans to achieve the optimal match. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned monitoring unit, Monitoring competition between generative AI and humans to ensure fair competition. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned promotion unit is Promote collaboration between generation AI and humans to improve overall work efficiency. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned reception unit is It estimates the user's emotions and adjusts the job registration method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned reception unit is When a user registers for a job, the system analyzes their past registration history and suggests the most suitable registration method. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned reception unit is When registering a job, 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 12) The aforementioned reception unit is It estimates the user's emotions and determines the priority of tasks to register based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned reception unit is When registering a job, the system prioritizes registering highly relevant jobs by considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned reception unit is When registering a job, the system analyzes the user's social media activity and registers relevant jobs. The system described in Appendix 1, characterized by the features described herein. (Note 15) The matching unit is It estimates the user's emotions and adjusts the matching criteria based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 16) The matching unit is When matching, we improve the accuracy of the matching process by considering the interrelationships between the jobs. The system described in Appendix 1, characterized by the features described herein. (Note 17) The matching unit is During the matching process, the attributes of the job registrants are taken into consideration when matching. The system described in Appendix 1, characterized by the features described herein. (Note 18) The matching unit is It estimates the user's sentiment and adjusts the order in which matching results are displayed based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 19) The matching unit is During the matching process, the geographical distribution of jobs will be taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 20) The matching unit is During the matching process, we improve the accuracy of the matching by referring to relevant literature related to the job. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned AI generation selection unit, It estimates the emotions of the generated AI and adjusts the job selection process based on the estimated emotions of the generated AI. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned AI generation selection unit, The generation AI analyzes its past selection history and proposes the optimal selection method. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned AI generation selection unit, The AI ​​selects tasks based on its current capabilities and status. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned AI generation selection unit, It estimates the emotions of the generated AI and determines the priority of tasks to select based on the estimated emotions of the generated AI. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned AI generation selection unit, When selecting jobs for generation, the system prioritizes highly relevant jobs by considering their geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned AI generation selection unit, When selecting a generation AI, refer to relevant literature related to the task to improve the accuracy of the selection. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned human selection unit, It estimates the user's emotions and adjusts how work is selected based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned human selection unit, It analyzes the user's past selection history and suggests the optimal selection method. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned human selection unit, Select tasks based on the user's current abilities and status. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned human selection unit, It estimates the user's emotions and determines the priority of tasks to select based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned human selection unit, When users select jobs, the system prioritizes selecting jobs that are highly relevant, taking into account the geographical location of those jobs. The system described in Appendix 1, characterized by the features described herein. (Note 32) The aforementioned human selection unit, When users make selections, we help them refer to relevant literature to improve the accuracy of their choices. The system described in Appendix 1, characterized by the features described herein. (Note 33) The evaluation unit, It estimates the emotions of the generated AI and adjusts the evaluation criteria based on the estimated emotions of the generated AI. The system described in Appendix 2, characterized by the features described herein. (Note 34) The evaluation unit, During evaluation, we refer to the past performance data of the generating AI to improve the accuracy of the evaluation. The system described in Appendix 2, characterized by the features described herein. (Note 35) The evaluation unit, During evaluation, the assessment is based on the current capabilities and state of the generating AI. The system described in Appendix 2, characterized by the features described herein. (Note 36) The evaluation unit, It estimates the emotions of the generated AI and adjusts how the evaluation results are displayed based on the estimated emotions of the generated AI. The system described in Appendix 2, characterized by the features described herein. (Note 37) The evaluation unit, During evaluation, the geographical distribution of the generated AI will be taken into consideration. The system described in Appendix 2, characterized by the features described herein. (Note 38) The evaluation unit, During evaluation, we refer to relevant literature on generative AI to improve the accuracy of the evaluation. The system described in Appendix 2, characterized by the features described herein. (Note 39) The aforementioned monitoring unit, It estimates emotions using generative AI and human emotions, and adjusts monitoring standards based on the estimated emotions. The system described in Appendix 4, characterized by the features described herein. (Note 40) The aforementioned monitoring unit, During monitoring, the accuracy of the monitoring is improved by referencing historical competition data from both generated AI and human observers. The system described in Appendix 4, characterized by the features described herein. (Note 41) The aforementioned monitoring unit, During monitoring, the monitoring is performed based on the current capabilities and status of both the generated AI and the human monitor. The system described in Appendix 4, characterized by the features described herein. (Note 42) The aforementioned monitoring unit, It estimates both generated AI and human emotions, and adjusts how monitoring results are displayed based on the estimated emotions. The system according to appended note 4, characterized by the above. (Appended note 43) The monitoring unit Performs monitoring considering the geographical distribution of the generative AI and humans during monitoring The system according to appended note 4, characterized by the above. (Appended note 44) The monitoring unit Improves the accuracy of monitoring by referring to the relevant literature of the generative AI and humans during monitoring The system according to appended note 4, characterized by the above. (Appended note 45) The promotion unit Estimates the emotions of the generative AI and humans, and adjusts the method of promoting cooperation based on the estimated emotions The system according to appended note 5, characterized by the above. (Appended note 46) The promotion unit Improves the accuracy of promotion by referring to the past cooperation data of the generative AI and humans during cooperation promotion The system according to appended note 5, characterized by the above. (Appended note 47) The promotion unit Performs promotion based on the current capabilities and states of the generative AI and humans during cooperation promotion The system according to appended note 5, characterized by the above. (Appended note 48) The promotion unit Estimates the emotions of the generative AI and humans, and determines the priority of cooperation based on the estimated emotions The system according to appended note, characterized by the above. (Appended note 49) The promotion unit Performs promotion considering the geographical distribution of the generative AI and humans during cooperation promotion The system according to appended note 5, characterized by the above. (Appended note 50) The promotion unit Improves the accuracy of promotion by referring to the relevant literature of the generative AI and humans during cooperation promotion The system according to appended note 5, characterized by the above.

Explanation of reference signs

[0218] 10, 210, 310, 410 data processing systems 12 data processing device 14 smart device 214 smart glasses 314 headset-type terminal 414 robot

Claims

1. The reception desk where you register for work, A matching unit that matches the jobs registered by the reception unit with the generation AI and humans, A generation AI selection unit where the generation AI selects a job, It includes a human selection unit where humans choose jobs. A system characterized by the following features.

2. It includes an evaluation unit that assesses the capabilities of the generating AI. The system according to feature 1.

3. It has an evaluation unit that assesses human capabilities. The system according to feature 1.

4. It includes a monitoring unit to oversee the competition between generating AI and humans. The system according to feature 1.

5. It is equipped with a facilitator that promotes collaboration between generating AI and humans. The system according to feature 1.

6. The matching unit is Evaluate the capabilities of both the generating AI and humans to achieve the optimal match. The system according to feature 1.

7. The aforementioned monitoring unit, Monitoring competition between generative AI and humans to ensure fair competition. The system according to feature 4.

8. The aforementioned promotion unit is Promoting collaboration between generational AI and humans to improve overall work efficiency. The system according to claim 5, characterized in that it is the same as described in claim 5.

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