system

Abstract

The system according to this embodiment aims to achieve optimal matching by comprehensively considering the skills, experience, culture, and values ​​of freelancers. [Solution] The system according to the embodiment comprises a collection unit, an analysis unit, a matching unit, and a recommendation unit. The collection unit collects the resumes, achievements, and personalities of freelancers. The analysis unit analyzes the data collected by the collection unit and analyzes the client's needs and culture. The matching unit automatically matches the most suitable profiles based on the data analyzed by the analysis unit. The recommendation unit proposes projects based on the profiles matched by the matching unit, obtains feedback, and makes improvements.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, since freelancer matching is performed based on numerical values and simple skills, there is a problem that it is difficult to find high-quality projects.

[0005] The system according to the embodiment aims to achieve optimal matching by comprehensively considering the skills, achievements, culture, and values of freelancers.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a data collection unit, an analysis unit, a matching unit, and a recommendation unit. The data collection unit collects the resumes, achievements, and personalities of freelancers. The analysis unit analyzes the data collected by the data collection unit to analyze the client's needs and culture. The matching unit automatically matches the most suitable profiles based on the data analyzed by the analysis unit. The recommendation unit proposes projects based on the profiles matched by the matching unit, obtains feedback, and makes improvements. [Effects of the Invention]

[0007] The system according to this embodiment can achieve optimal matching by comprehensively considering the skills, experience, culture, and values ​​of freelancers. [Brief explanation of the drawing]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The freelance matching system according to an embodiment of the present invention is a system that makes it easier to find high-quality projects by collecting the resumes, achievements, and personalities of freelancers and analyzing the needs and culture of clients. This system uses an AI agent to achieve matching that comprehensively considers skills, achievements, culture, and values. For example, it delves into the resumes, achievements, and personalities of freelancers and analyzes the needs and culture of clients. In this data analysis phase, the AI ​​agent analyzes the profile data of freelancers and clients in detail. For example, it analyzes the success rate of freelancers' past projects and their suitability for the client's corporate culture. Next, a matching phase is performed in which the AI ​​agent automatically connects the most suitable profiles. Here, the AI ​​agent comprehensively evaluates the profiles of freelancers and clients and performs the optimal matching. For example, a freelancer with a specific skill set is determined to be the best fit for a specific project. Furthermore, a recommendation phase follows in which the AI ​​agent proposes projects and receives feedback to improve the system. Here, the AI ​​agent proposes the most suitable freelancer to the client and improves the matching algorithm based on the client's feedback. For example, if a client is satisfied with a proposed freelancer, that feedback can be used to improve the accuracy of future matching. This system makes it easier for freelancers to find high-quality projects, and allows clients to assign the most suitable talent. For instance, an AI agent comprehensively evaluates the freelancer's skill set and the client's project requirements to make the best match, resulting in high-quality deliverables. The strengths of generative AI include improved accuracy in profile data analysis, real-time optimization and scaling capabilities, and a personalized touch through next-generation matching algorithms. This provides a new platform that delivers increased efficiency and quality, transforming the future for both freelancers and clients.This makes it easier for freelance matching systems to find high-quality projects, and allows clients to assign the most suitable talent.

[0029] The freelance matching system according to this embodiment comprises a data collection unit, an analysis unit, a matching unit, and a recommendation unit. The data collection unit collects the freelancer's resume, achievements, and personality. For example, the data collection unit collects information such as the freelancer's work history, skill set, and personality traits. The data collection unit can also collect data such as the success rate of the freelancer's past projects and their suitability for the client's corporate culture. For example, the data collection unit calculates the success rate based on the freelancer's past project completion rate and client satisfaction. The analysis unit analyzes the data collected by the data collection unit to analyze the client's needs and culture. For example, the analysis unit analyzes the client's project requirements, corporate values, and work style. The analysis unit can also perform a detailed analysis of the freelancer and client profiles based on the collected data. For example, the analysis unit performs a detailed analysis based on the type of data, analysis method, and evaluation criteria. The matching unit automatically matches the most suitable profiles based on the data analyzed by the analysis unit. The matching unit determines, for example, that a freelancer with a specific skill set is best suited for a particular project. The matching unit can also perform optimal matching based on the freelancer's skill set and the client's project requirements. For example, the matching unit connects the best profiles based on skill matching and cultural compatibility. The recommendation unit proposes projects based on the profiles connected by the matching unit, obtains feedback, and makes improvements. For example, the recommendation unit proposes the best freelancer to the client and improves the matching algorithm based on client feedback. Furthermore, the recommendation unit can use generative AI to improve the accuracy of profile data analysis, achieve real-time optimization and scaling capabilities, and provide a personal touch through next-generation matching algorithms. For example, the recommendation unit uses generative AI to improve the accuracy of profile data analysis and performs real-time optimization and scaling. As a result, the freelance matching system according to this embodiment can comprehensively evaluate the profiles of freelancers and clients and achieve optimal matching.

[0030] The data collection department collects resumes, achievements, and personality information from freelancers. Specifically, it gathers information such as the freelancer's work history, skill set, and personality traits. For example, it evaluates what projects the freelancer has participated in in the past, what roles they played, what skills they possess, and personality traits such as leadership, teamwork, and communication skills. The data collection department can also collect data such as the success rate of the freelancer's past projects and their fit with the client's corporate culture. For example, it can calculate the success rate based on the completion rate of projects the freelancer has handled in the past and the client satisfaction level. This allows for an objective evaluation of the freelancer's reliability and track record. Furthermore, the data collection department can collect data such as the freelancer's social media activity and online portfolio to create a more comprehensive profile. This allows for a more detailed understanding of the freelancer's expertise and personality. The data collection department centrally manages this data and makes it accessible to the analysis and matching departments. Data can be collected not only from information provided by the freelancer themselves, but also from external data sources. For example, by collecting data from review sites for projects in which freelancers have participated, or from industry rating agencies, more reliable information can be obtained. This allows the data collection unit to evaluate freelancers' profiles from multiple perspectives, improving the overall accuracy and reliability of the system.

[0031] The analysis department analyzes data collected by the data collection department to understand client needs and culture. Specifically, it analyzes client project requirements, corporate values, and work styles. For example, it analyzes in detail the client's required skill set, years of experience, project size and duration, and budget. It also analyzes corporate values, culture, and work styles to evaluate whether a freelancer is a good fit for the company. The analysis department can also perform detailed profiles of freelancers and clients based on the collected data. For example, it performs detailed analysis based on data type, analysis method, and evaluation criteria. By introducing AI-based analysis methods, it is possible to quickly grasp data patterns and trends and perform more accurate analysis. For example, natural language processing technology is used to analyze freelancer resumes and client project requirements to extract important keywords and phrases. Furthermore, machine learning algorithms can be used to learn highly successful matching patterns based on past matching data and apply them to new matches. As a result, the analysis department can quickly and accurately analyze collected data and comprehensively evaluate client needs and freelancer characteristics. Furthermore, the analysis unit can utilize historical data and statistical information to perform long-term trend and risk assessments. For example, it can predict fluctuations in demand for specific skill sets or industries, improving the accuracy of future matching. The analysis unit can also use anomaly detection algorithms to detect unusual patterns or abnormal data, issuing early warnings. This allows the analysis unit to not only provide real-time situational awareness but also handle long-term risk management and anomaly detection, improving the overall reliability and security of the system.

[0032] The matching unit automatically connects the most suitable profiles based on data analyzed by the analysis unit. Specifically, it determines that a freelancer with a particular skill set is best suited for a particular project. For example, it performs optimal matching based on the freelancer's skill set and the client's project requirements. It connects the most suitable profiles based on skill matching and cultural compatibility. The matching unit uses AI to analyze the profiles of freelancers and clients in detail to achieve optimal matching. For example, it can use machine learning algorithms to learn highly successful matching patterns based on past matching data and apply them to new matches. It also uses natural language processing technology to analyze freelancers' resumes and clients' project requirements, extracting important keywords and phrases. This allows the matching unit to comprehensively evaluate the profiles of freelancers and clients and achieve optimal matching. Furthermore, the matching unit can continuously modify matching results based on real-time updated data to respond to the latest situations. For example, if a freelancer's skill set or a client's project requirements change, the matching unit immediately incorporates the new data and updates the matching results. Furthermore, the matching unit can perform more accurate matching by taking into account the characteristics of each region and past matching history. As a result, the matching unit can always provide highly accurate matching based on the latest information, supporting quick and appropriate responses.

[0033] The recommendation department proposes projects based on profiles linked by the matching department, and improves them based on feedback. Specifically, it proposes the most suitable freelancers to clients and improves the matching algorithm based on client feedback. For example, it collects feedback on how clients evaluated the proposed freelancers, as well as the progress and results of the projects. This allows the recommendation department to continuously improve the accuracy of the matching algorithm. The recommendation department can also improve the accuracy of profile data analysis using generative AI, achieve real-time optimization and scaling capabilities, and provide a personal touch with next-generation matching algorithms. For example, it uses generative AI to improve the accuracy of profile data analysis and perform real-time optimization and scaling. The generative AI analyzes the profile data of freelancers and clients in detail and generates new algorithms to achieve optimal matching. This allows the recommendation department to achieve more accurate matching and improve client and freelancer satisfaction. Furthermore, the recommendation department can continuously improve the matching algorithm based on feedback and improve the overall system performance. For example, it adjusts the parameters of the matching algorithm based on client feedback to achieve more accurate matching. The recommendation department can also reliably transmit information using multiple communication methods. For example, by using email, chat, and notification systems in combination, important information can be reliably delivered. This allows the recommendation department to provide information to users quickly and reliably, improving the reliability and security of the entire system.

[0034] The data collection unit can collect data such as the success rate of a freelancer's past projects and their fit with the client's corporate culture. For example, to calculate the success rate of a freelancer's past projects, the data collection unit collects the percentage of completed projects and client satisfaction. The data collection unit can also collect data such as the company's values, work style, and communication style to evaluate their fit with the client's corporate culture. For example, the data collection unit collects detailed data on freelancers who are a good fit for the client's corporate culture. This allows the data collection unit to collect data that takes into account the success rate of a freelancer's past projects and their fit with the client's corporate culture. Some or all of the processing described above in the data collection unit may be performed using AI, for example, or not. For example, the data collection unit can input the success rate of a freelancer's past projects into an AI and have the AI ​​calculate the success rate.

[0035] The analysis unit can perform detailed analyses of freelancers' and clients' profiles based on the collected data. For example, the analysis unit can perform detailed analyses based on data such as the freelancer's work history, skill set, and personality traits. The analysis unit can also perform detailed analyses based on data such as the client's project requirements, company values, and work style. For example, the analysis unit can perform detailed analyses based on data type, analysis method, and evaluation criteria. This allows the analysis unit to perform more accurate matching by analyzing the profiles of freelancers and clients in detail. Some or all of the above processes in the analysis unit may be performed using AI, for example, or not. For example, the analysis unit can input the collected data into AI and have the AI ​​perform a detailed analysis of the profiles.

[0036] The matching unit can determine that a freelancer with a specific skill set is best suited for a particular project. For example, the matching unit performs the best matching based on the freelancer's skill set and the client's project requirements. The matching unit can also identify freelancers with the necessary skill sets based on the client's project requirements. For example, the matching unit connects the best profiles based on skill matching and cultural compatibility. This allows the matching unit to achieve optimal matching by determining that a freelancer with a specific skill set is best suited for a particular project. Some or all of the above processes in the matching unit may be performed using AI, or not. For example, the matching unit can input the freelancer's skill set and the client's project requirements into an AI and have the AI ​​perform the optimal matching.

[0037] The recommendation department can propose the most suitable freelancer to the client and improve the matching algorithm based on client feedback. For example, the recommendation department can propose the most suitable freelancer to the client and improve the matching algorithm based on client feedback. Furthermore, if the client is satisfied, the recommendation department can use that feedback to improve the accuracy of the next matching. For example, if the client is dissatisfied, the recommendation department can use that feedback to improve the matching algorithm. This allows the recommendation department to improve the accuracy of the next matching by improving the matching algorithm based on client feedback. Some or all of the above processes in the recommendation department may be performed using AI, or not. For example, the recommendation department can input client feedback into AI and have the AI ​​improve the matching algorithm.

[0038] The recommendation system can improve the accuracy of profile data analysis using generative AI, achieve real-time optimization and scaling capabilities, and provide a personalized touch through next-generation matching algorithms. For example, the recommendation system can improve the accuracy of profile data analysis using generative AI and perform real-time optimization and scaling. Furthermore, the recommendation system can use next-generation matching algorithms to customize to individual needs and incorporate user feedback. For example, the recommendation system can improve the accuracy of profile data analysis using generative AI and perform real-time optimization and scaling. This enables the recommendation system to improve the accuracy of profile data analysis, achieve real-time optimization and scaling capabilities, and provide a personalized touch through next-generation matching algorithms by using generative AI. Some or all of the above processes in the recommendation system may be performed using AI, or not. For example, the recommendation system can improve the accuracy of profile data analysis using generative AI and perform real-time optimization and scaling. This enables the recommendation system to improve the accuracy of profile data analysis, achieve real-time optimization and scaling capabilities, and provide a personalized touch through next-generation matching algorithms by using generative AI.

[0039] The data collection unit can analyze the success rate of a freelancer's past projects and determine the priority of the data to collect. For example, the data collection unit can prioritize collecting data on projects with high success rates to understand the freelancer's strengths. The data collection unit can also collect data on projects with low success rates to identify areas for improvement. For example, the data collection unit can compare data from high-success and low-success projects to find areas for growth in the freelancer. This allows the data collection unit to determine the priority of the data to collect by analyzing the success rate of a freelancer's past projects. Some or all of the above processing in the data collection unit may be performed using AI, for example, or not. For example, the data collection unit can input the success rate of a freelancer's past projects into an AI and have the AI ​​perform the data prioritization.

[0040] The data collection unit can assess the suitability of freelancers to the client's corporate culture and adjust the level of detail of the data it collects. For example, the data collection unit can collect detailed data on freelancers who are a good fit for the client's corporate culture. Alternatively, the data collection unit can simplify the collection of data on freelancers who are not a good fit for the client's corporate culture. For example, the data collection unit can assess the suitability of freelancers to the client's corporate culture and prioritize the collection of data on freelancers with a high degree of suitability. This allows the data collection unit to adjust the level of detail of the data it collects by assessing the suitability for the client's corporate culture. Some or all of the above processing in the data collection unit may be performed using AI, for example, or not. For example, the data collection unit can input the suitability for the client's corporate culture into the AI ​​and have the AI ​​perform the adjustment of the level of detail of the data.

[0041] The data collection unit can prioritize the collection of highly relevant data by considering the geographical location information of freelancers. For example, if a freelancer lives in a specific region, the data collection unit will prioritize the collection of project data related to that region. The data collection unit can also collect relevant project data by considering the range of movement the freelancer can travel. For example, the data collection unit will collect data tailored to the demand in each region based on the freelancer's geographical location information. In this way, the data collection unit can prioritize the collection of highly relevant data by considering the freelancer's geographical location information. Some or all of the above processing in the data collection unit may be performed using AI, for example, or not using AI. For example, the data collection unit can input the freelancer's geographical location information into AI and have the AI ​​perform the collection of highly relevant data.

[0042] The data collection unit can analyze the social media activities of freelancers and collect relevant data. For example, the data collection unit can analyze the content of a freelancer's social media activities and collect data on their skills and achievements. The data collection unit can also collect data on influence based on the number of followers and engagement rates of freelancers on social media. For example, the data collection unit can analyze the content of a freelancer's social media posts and collect data on their personality and values. In this way, the data collection unit can collect relevant data by analyzing the social media activities of freelancers. Some or all of the above processing in the data collection unit may be performed using AI, for example, or not using AI. For example, the data collection unit can input the freelancer's social media activities into AI and have the AI ​​collect the relevant data.

[0043] The analysis unit can perform a detailed analysis of freelancers' skill sets and clients' project requirements based on the collected data. For example, the analysis unit can perform a detailed analysis of freelancers' skill sets and make the best match with the client's project requirements. The analysis unit can also perform a detailed analysis of the client's project requirements and identify the necessary skill sets. For example, the analysis unit can compare freelancers' skill sets with the client's project requirements and make the best match. In this way, the analysis unit can make the best match by performing a detailed analysis of freelancers' skill sets and clients' project requirements. Some or all of the above processing in the analysis unit may be performed using AI, for example, or not using AI. For example, the analysis unit can input the collected data into AI and have the AI ​​perform a detailed analysis of skill sets and project requirements.

[0044] The analysis unit can improve the accuracy of its analysis based on the success rate of a freelancer's past projects. For example, the analysis unit can evaluate a freelancer's skill set based on the success rate of their past projects. It can also evaluate a freelancer's suitability for a client's project requirements based on the success rate of their past projects. For example, the analysis unit can improve its algorithms to enhance the accuracy of its analysis based on the success rate of a freelancer's past projects. This allows the analysis unit to perform more accurate analyses by improving the accuracy of its analysis based on the success rate of a freelancer's past projects. Some or all of the above processes in the analysis unit may be performed using AI, or not. For example, the analysis unit can input the success rate of a freelancer's past projects into an AI and have the AI ​​perform the analysis accuracy improvement.

[0045] The analysis unit can classify the analysis results by region, taking into account the geographical location information of freelancers. For example, the analysis unit can display analysis results tailored to the demand in each region based on the geographical location information of freelancers. The analysis unit can also analyze the trends in skill sets by region based on the geographical location information of freelancers. For example, the analysis unit can analyze the project success rate by region based on the geographical location information of freelancers. In this way, the analysis unit can classify the analysis results by region by taking into account the geographical location information of freelancers. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without using AI. For example, the analysis unit can input the geographical location information of freelancers into AI and have the AI ​​perform the classification of the analysis results by region.

[0046] The analysis unit can analyze the social media activities of freelancers and reflect the results in the analysis. For example, the analysis unit can analyze the content of a freelancer's social media activities and reflect data on their skills and achievements. The analysis unit can also reflect data on their influence based on the number of followers and engagement rate on social media. For example, the analysis unit can analyze the content of a freelancer's social media posts and reflect data on their personality and values. In this way, the analysis unit can analyze the social media activities of freelancers and reflect the results in the analysis. Some or all of the above processing in the analysis unit may be performed using AI, for example, or not using AI. For example, the analysis unit can input the freelancer's social media activities into AI and have AI perform the reflection of the results in the analysis.

[0047] The matching unit can perform optimal matching based on the freelancer's skill set and the client's project requirements. For example, the matching unit can perform optimal matching based on the freelancer's skill set and the client's project requirements. The matching unit can also identify freelancers with the necessary skill sets based on the client's project requirements. For example, the matching unit can compare the freelancer's skill set and the client's project requirements to perform optimal matching. In this way, the matching unit can perform more appropriate matching by performing optimal matching based on the freelancer's skill set and the client's project requirements. 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 freelancer's skill set and the client's project requirements into AI and have the AI ​​perform optimal matching.

[0048] The matching unit can improve the accuracy of matching by considering the success rate of a freelancer's past projects. For example, the matching unit evaluates the skill set based on the success rate of a freelancer's past projects. The matching unit can also evaluate the suitability of a freelancer to the client's project requirements based on the success rate of a freelancer's past projects. For example, the matching unit improves the algorithm for improving matching accuracy based on the success rate of a freelancer's past projects. In this way, the matching unit can improve the accuracy of matching by considering the success rate of a freelancer's past projects. 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 success rate of a freelancer's past projects into an AI and have the AI ​​perform the improvement of matching accuracy.

[0049] The matching unit can perform optimal matching for each region by considering the geographical location information of freelancers. For example, the matching unit can perform matching according to the demand in each region based on the geographical location information of freelancers. The matching unit can also analyze the trends in skill sets in each region based on the geographical location information of freelancers and perform optimal matching. For example, the matching unit can analyze the project success rate in each region based on the geographical location information of freelancers and perform optimal matching. In this way, the matching unit can perform optimal matching for each region by considering the geographical location information of freelancers. Some or all of the above processing in the matching unit may be performed using AI, for example, or not using AI. For example, the matching unit can input the geographical location information of freelancers into AI and have the AI ​​perform optimal matching for each region.

[0050] The matching unit can analyze freelancers' social media activity and reflect it in the matching results. For example, the matching unit can analyze the content of freelancers' social media activities and reflect data on their skills and achievements. The matching unit can also reflect data on influence based on the number of followers and engagement rate of freelancers on social media. For example, the matching unit can analyze the content of freelancers' social media posts and reflect data on their personality and values. In this way, the matching unit can analyze freelancers' social media activity and reflect it in the matching results. Some or all of the above processing in the matching unit may be performed using AI, for example, or not. For example, the matching unit can input freelancers' social media activity into AI and have the AI ​​perform the reflection of it in the matching results.

[0051] The recommendation department can improve its recommendation algorithm based on feedback from clients. For example, if a client is satisfied, the recommendation department can improve the recommendation algorithm based on that feedback. Similarly, if a client is dissatisfied, the recommendation department can also improve the recommendation algorithm based on that feedback. For example, the recommendation department can collect feedback from clients and improve the accuracy of the recommendation algorithm. This improves the accuracy of recommendations by improving the recommendation algorithm based on client feedback. Some or all of the above processes in the recommendation department may be performed using AI, or not. For example, the recommendation department can input client feedback into an AI and have the AI ​​improve the recommendation algorithm.

[0052] The recommendation system can improve the accuracy of its recommendations by considering the success rate of a freelancer's past projects. For example, the recommendation system can improve the accuracy of its recommendations based on the success rate of a freelancer's past projects. The recommendation system can also evaluate the suitability of a freelancer to a client's project requirements based on the success rate of a freelancer's past projects. For example, the recommendation system can improve its recommendation algorithm based on the success rate of a freelancer's past projects. This improves the accuracy of the recommendations by considering the success rate of a freelancer's past projects. Some or all of the above processes in the recommendation system may be performed using AI, or not. For example, the recommendation system can input the success rate of a freelancer's past projects into an AI and have the AI ​​perform the recommendation accuracy improvement.

[0053] The recommendation department can make optimal recommendations for each region by taking into account the client's geographical location information. For example, the recommendation department can make recommendations that meet the needs of each region based on the client's geographical location information. Furthermore, the recommendation department can analyze the trends in skill sets for each region based on the client's geographical location information and make optimal recommendations. For example, the recommendation department can analyze the project success rate for each region based on the client's geographical location information and make optimal recommendations. In this way, the recommendation department can make optimal recommendations for each region by considering the client's geographical location information. Some or all of the above processes in the recommendation department may be performed using AI, or not. For example, the recommendation department can input the client's geographical location information into AI and have the AI ​​perform optimal recommendations for each region.

[0054] The recommendation system can analyze a freelancer's social media activity and reflect it in the recommendation results. For example, the recommendation system can analyze a freelancer's social media activity and reflect data on their skills and achievements. It can also reflect data on their influence based on the number of followers and engagement rate on social media. For example, the recommendation system can analyze a freelancer's social media posts and reflect data on their personality and values. In this way, the recommendation system can analyze a freelancer's social media activity and reflect it in the recommendation results. Some or all of the above processing in the recommendation system may be performed using AI, for example, or not. For example, the recommendation system can input a freelancer's social media activity into AI and have the AI ​​perform the reflection of it in the recommendation results.

[0055] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0056] The analysis unit can evaluate a freelancer's growth rate based on the success rate of their past projects. For example, the analysis unit analyzes fluctuations in a freelancer's past project success rate and calculates the growth rate. Furthermore, the analysis unit can evaluate the evolution of a freelancer's skill set and reflect this in the growth rate. For instance, if a freelancer acquires a new skill, the analysis unit reflects its impact in the growth rate. This allows the analysis unit to perform more appropriate matching by evaluating the freelancer's growth rate.

[0057] The matching system can perform optimal matching for each region by considering the geographical location of freelancers. For example, it can perform matching based on the demand in each region based on the geographical location of freelancers. It can also analyze the trends in skill sets in each region based on the geographical location of freelancers and perform optimal matching. In this way, the matching system can perform optimal matching for each region by considering the geographical location of freelancers.

[0058] The analytics department can analyze freelancers' social media activities and reflect the findings in the analysis results. For example, it can analyze the content of a freelancer's social media activities and reflect data on their skills and achievements. It can also reflect data on their influence based on the number of followers and engagement rate on social media. In this way, the analytics department can analyze freelancers' social media activities and reflect the findings in the analysis results.

[0059] The matching system can improve the accuracy of matching by considering the success rate of freelancers' past projects. For example, it can evaluate skill sets based on the success rate of freelancers' past projects. It can also evaluate suitability for client project requirements based on the success rate of freelancers' past projects. In this way, the matching system can improve the accuracy of matching by considering the success rate of freelancers' past projects.

[0060] The recommendation department can make optimal recommendations for each region by considering the client's geographical location. For example, it can make recommendations based on the client's geographical location to meet the needs of each region. It can also analyze the trends in skill sets for each region based on the client's geographical location to make optimal recommendations. In this way, the recommendation department can make optimal recommendations for each region by considering the client's geographical location.

[0061] The following briefly describes the processing flow for example form 1.

[0062] Step 1: The collection team gathers the freelancer's resume, experience, and personality. For example, they collect information such as the freelancer's work history, skill set, personality traits, success rate of past projects, and suitability for the client's corporate culture. Step 2: The analysis unit analyzes the data collected by the data collection unit to analyze client needs and culture. For example, it analyzes the client's project requirements, corporate values, and work style to conduct a detailed analysis of freelancer and client profiles. Step 3: The matching unit automatically connects the most suitable profiles based on the data analyzed by the analysis unit. For example, it may determine that a freelancer with a specific skill set is best suited for a particular project and connect the most suitable profiles based on skill matching and cultural compatibility. Step 4: The recommendation department proposes projects based on the profiles linked by the matching department, obtains feedback, and makes improvements. For example, it proposes the most suitable freelancer to a client and improves the matching algorithm based on client feedback. It also uses generative AI to improve the accuracy of profile data analysis and performs real-time optimization and scaling.

[0063] (Example of form 2) The freelance matching system according to an embodiment of the present invention is a system that makes it easier to find high-quality projects by collecting the resumes, achievements, and personalities of freelancers and analyzing the needs and culture of clients. This system uses an AI agent to achieve matching that comprehensively considers skills, achievements, culture, and values. For example, it delves into the resumes, achievements, and personalities of freelancers and analyzes the needs and culture of clients. In this data analysis phase, the AI ​​agent analyzes the profile data of freelancers and clients in detail. For example, it analyzes the success rate of freelancers' past projects and their suitability for the client's corporate culture. Next, a matching phase is performed in which the AI ​​agent automatically connects the most suitable profiles. Here, the AI ​​agent comprehensively evaluates the profiles of freelancers and clients and performs the optimal matching. For example, a freelancer with a specific skill set is determined to be the best fit for a specific project. Furthermore, a recommendation phase follows in which the AI ​​agent proposes projects and receives feedback to improve the system. Here, the AI ​​agent proposes the most suitable freelancer to the client and improves the matching algorithm based on the client's feedback. For example, if a client is satisfied with a proposed freelancer, that feedback can be used to improve the accuracy of future matching. This system makes it easier for freelancers to find high-quality projects, and allows clients to assign the most suitable talent. For instance, an AI agent comprehensively evaluates the freelancer's skill set and the client's project requirements to make the best match, resulting in high-quality deliverables. The strengths of generative AI include improved accuracy in profile data analysis, real-time optimization and scaling capabilities, and a personalized touch through next-generation matching algorithms. This provides a new platform that delivers increased efficiency and quality, transforming the future for both freelancers and clients.This makes it easier for freelance matching systems to find high-quality projects, and allows clients to assign the most suitable talent.

[0064] The freelance matching system according to this embodiment comprises a data collection unit, an analysis unit, a matching unit, and a recommendation unit. The data collection unit collects the freelancer's resume, achievements, and personality. For example, the data collection unit collects information such as the freelancer's work history, skill set, and personality traits. The data collection unit can also collect data such as the success rate of the freelancer's past projects and their suitability for the client's corporate culture. For example, the data collection unit calculates the success rate based on the freelancer's past project completion rate and client satisfaction. The analysis unit analyzes the data collected by the data collection unit to analyze the client's needs and culture. For example, the analysis unit analyzes the client's project requirements, corporate values, and work style. The analysis unit can also perform a detailed analysis of the freelancer and client profiles based on the collected data. For example, the analysis unit performs a detailed analysis based on the type of data, analysis method, and evaluation criteria. The matching unit automatically matches the most suitable profiles based on the data analyzed by the analysis unit. The matching unit determines, for example, that a freelancer with a specific skill set is best suited for a particular project. The matching unit can also perform optimal matching based on the freelancer's skill set and the client's project requirements. For example, the matching unit connects the best profiles based on skill matching and cultural compatibility. The recommendation unit proposes projects based on the profiles connected by the matching unit, obtains feedback, and makes improvements. For example, the recommendation unit proposes the best freelancer to the client and improves the matching algorithm based on client feedback. Furthermore, the recommendation unit can use generative AI to improve the accuracy of profile data analysis, achieve real-time optimization and scaling capabilities, and provide a personal touch through next-generation matching algorithms. For example, the recommendation unit uses generative AI to improve the accuracy of profile data analysis and performs real-time optimization and scaling. As a result, the freelance matching system according to this embodiment can comprehensively evaluate the profiles of freelancers and clients and achieve optimal matching.

[0065] The data collection department collects resumes, achievements, and personality information from freelancers. Specifically, it gathers information such as the freelancer's work history, skill set, and personality traits. For example, it evaluates what projects the freelancer has participated in in the past, what roles they played, what skills they possess, and personality traits such as leadership, teamwork, and communication skills. The data collection department can also collect data such as the success rate of the freelancer's past projects and their fit with the client's corporate culture. For example, it can calculate the success rate based on the completion rate of projects the freelancer has handled in the past and the client satisfaction level. This allows for an objective evaluation of the freelancer's reliability and track record. Furthermore, the data collection department can collect data such as the freelancer's social media activity and online portfolio to create a more comprehensive profile. This allows for a more detailed understanding of the freelancer's expertise and personality. The data collection department centrally manages this data and makes it accessible to the analysis and matching departments. Data can be collected not only from information provided by the freelancer themselves, but also from external data sources. For example, by collecting data from review sites for projects in which freelancers have participated, or from industry rating agencies, more reliable information can be obtained. This allows the data collection unit to evaluate freelancers' profiles from multiple perspectives, improving the overall accuracy and reliability of the system.

[0066] The analysis department analyzes data collected by the data collection department to understand client needs and culture. Specifically, it analyzes client project requirements, corporate values, and work styles. For example, it analyzes in detail the client's required skill set, years of experience, project size and duration, and budget. It also analyzes corporate values, culture, and work styles to evaluate whether a freelancer is a good fit for the company. The analysis department can also perform detailed profiles of freelancers and clients based on the collected data. For example, it performs detailed analysis based on data type, analysis method, and evaluation criteria. By introducing AI-based analysis methods, it is possible to quickly grasp data patterns and trends and perform more accurate analysis. For example, natural language processing technology is used to analyze freelancer resumes and client project requirements to extract important keywords and phrases. Furthermore, machine learning algorithms can be used to learn highly successful matching patterns based on past matching data and apply them to new matches. As a result, the analysis department can quickly and accurately analyze collected data and comprehensively evaluate client needs and freelancer characteristics. Furthermore, the analysis unit can utilize historical data and statistical information to perform long-term trend and risk assessments. For example, it can predict fluctuations in demand for specific skill sets or industries, improving the accuracy of future matching. The analysis unit can also use anomaly detection algorithms to detect unusual patterns or abnormal data, issuing early warnings. This allows the analysis unit to not only provide real-time situational awareness but also handle long-term risk management and anomaly detection, improving the overall reliability and security of the system.

[0067] The matching unit automatically connects the most suitable profiles based on data analyzed by the analysis unit. Specifically, it determines that a freelancer with a particular skill set is best suited for a particular project. For example, it performs optimal matching based on the freelancer's skill set and the client's project requirements. It connects the most suitable profiles based on skill matching and cultural compatibility. The matching unit uses AI to analyze the profiles of freelancers and clients in detail to achieve optimal matching. For example, it can use machine learning algorithms to learn highly successful matching patterns based on past matching data and apply them to new matches. It also uses natural language processing technology to analyze freelancers' resumes and clients' project requirements, extracting important keywords and phrases. This allows the matching unit to comprehensively evaluate the profiles of freelancers and clients and achieve optimal matching. Furthermore, the matching unit can continuously modify matching results based on real-time updated data to respond to the latest situations. For example, if a freelancer's skill set or a client's project requirements change, the matching unit immediately incorporates the new data and updates the matching results. Furthermore, the matching unit can perform more accurate matching by taking into account the characteristics of each region and past matching history. As a result, the matching unit can always provide highly accurate matching based on the latest information, supporting quick and appropriate responses.

[0068] The recommendation department proposes projects based on profiles linked by the matching department, and improves them based on feedback. Specifically, it proposes the most suitable freelancers to clients and improves the matching algorithm based on client feedback. For example, it collects feedback on how clients evaluated the proposed freelancers, as well as the progress and results of the projects. This allows the recommendation department to continuously improve the accuracy of the matching algorithm. The recommendation department can also improve the accuracy of profile data analysis using generative AI, achieve real-time optimization and scaling capabilities, and provide a personal touch with next-generation matching algorithms. For example, it uses generative AI to improve the accuracy of profile data analysis and perform real-time optimization and scaling. The generative AI analyzes the profile data of freelancers and clients in detail and generates new algorithms to achieve optimal matching. This allows the recommendation department to achieve more accurate matching and improve client and freelancer satisfaction. Furthermore, the recommendation department can continuously improve the matching algorithm based on feedback and improve the overall system performance. For example, it adjusts the parameters of the matching algorithm based on client feedback to achieve more accurate matching. The recommendation department can also reliably transmit information using multiple communication methods. For example, by using email, chat, and notification systems in combination, important information can be reliably delivered. This allows the recommendation department to provide information to users quickly and reliably, improving the reliability and security of the entire system.

[0069] The data collection unit can collect data such as the success rate of a freelancer's past projects and their fit with the client's corporate culture. For example, to calculate the success rate of a freelancer's past projects, the data collection unit collects the percentage of completed projects and client satisfaction. The data collection unit can also collect data such as the company's values, work style, and communication style to evaluate their fit with the client's corporate culture. For example, the data collection unit collects detailed data on freelancers who are a good fit for the client's corporate culture. This allows the data collection unit to collect data that takes into account the success rate of a freelancer's past projects and their fit with the client's corporate culture. Some or all of the processing described above in the data collection unit may be performed using AI, for example, or not. For example, the data collection unit can input the success rate of a freelancer's past projects into an AI and have the AI ​​calculate the success rate.

[0070] The analysis unit can perform detailed analyses of freelancers' and clients' profiles based on the collected data. For example, the analysis unit can perform detailed analyses based on data such as the freelancer's work history, skill set, and personality traits. The analysis unit can also perform detailed analyses based on data such as the client's project requirements, company values, and work style. For example, the analysis unit can perform detailed analyses based on data type, analysis method, and evaluation criteria. This allows the analysis unit to perform more accurate matching by analyzing the profiles of freelancers and clients in detail. Some or all of the above processes in the analysis unit may be performed using AI, for example, or not. For example, the analysis unit can input the collected data into AI and have the AI ​​perform a detailed analysis of the profiles.

[0071] The matching unit can determine that a freelancer with a specific skill set is best suited for a particular project. For example, the matching unit performs the best matching based on the freelancer's skill set and the client's project requirements. The matching unit can also identify freelancers with the necessary skill sets based on the client's project requirements. For example, the matching unit connects the best profiles based on skill matching and cultural compatibility. This allows the matching unit to achieve optimal matching by determining that a freelancer with a specific skill set is best suited for a particular project. Some or all of the above processes in the matching unit may be performed using AI, or not. For example, the matching unit can input the freelancer's skill set and the client's project requirements into an AI and have the AI ​​perform the optimal matching.

[0072] The recommendation department can propose the most suitable freelancer to the client and improve the matching algorithm based on client feedback. For example, the recommendation department can propose the most suitable freelancer to the client and improve the matching algorithm based on client feedback. Furthermore, if the client is satisfied, the recommendation department can use that feedback to improve the accuracy of the next matching. For example, if the client is dissatisfied, the recommendation department can use that feedback to improve the matching algorithm. This allows the recommendation department to improve the accuracy of the next matching by improving the matching algorithm based on client feedback. Some or all of the above processes in the recommendation department may be performed using AI, or not. For example, the recommendation department can input client feedback into AI and have the AI ​​improve the matching algorithm.

[0073] The recommendation system can improve the accuracy of profile data analysis using generative AI, achieve real-time optimization and scaling capabilities, and provide a personalized touch through next-generation matching algorithms. For example, the recommendation system can improve the accuracy of profile data analysis using generative AI and perform real-time optimization and scaling. Furthermore, the recommendation system can use next-generation matching algorithms to customize to individual needs and incorporate user feedback. For example, the recommendation system can improve the accuracy of profile data analysis using generative AI and perform real-time optimization and scaling. This enables the recommendation system to improve the accuracy of profile data analysis, achieve real-time optimization and scaling capabilities, and provide a personalized touch through next-generation matching algorithms by using generative AI. Some or all of the above processes in the recommendation system may be performed using AI, or not. For example, the recommendation system can improve the accuracy of profile data analysis using generative AI and perform real-time optimization and scaling. This enables the recommendation system to improve the accuracy of profile data analysis, achieve real-time optimization and scaling capabilities, and provide a personalized touch through next-generation matching algorithms by using generative AI.

[0074] The data collection unit can estimate the freelancer's emotions and adjust the timing of data collection based on the estimated emotions. For example, if the freelancer is stressed, the data collection unit can temporarily delay data collection and resume collection when the freelancer is relaxed. Conversely, if the freelancer is relaxed, the data collection unit can actively collect data to obtain detailed information. For example, if the freelancer is busy, the data collection unit can minimize data collection and collect detailed information later. This allows the data collection unit to collect data at a more appropriate time by adjusting the timing of data collection based on the freelancer's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input the freelancer's emotion data into an AI and have the AI ​​adjust the timing of data collection.

[0075] The data collection unit can analyze the success rate of a freelancer's past projects and determine the priority of the data to collect. For example, the data collection unit can prioritize collecting data on projects with high success rates to understand the freelancer's strengths. The data collection unit can also collect data on projects with low success rates to identify areas for improvement. For example, the data collection unit can compare data from high-success and low-success projects to find areas for growth in the freelancer. This allows the data collection unit to determine the priority of the data to collect by analyzing the success rate of a freelancer's past projects. Some or all of the above processing in the data collection unit may be performed using AI, for example, or not. For example, the data collection unit can input the success rate of a freelancer's past projects into an AI and have the AI ​​perform the data prioritization.

[0076] The data collection unit can assess the suitability of freelancers to the client's corporate culture and adjust the level of detail of the data it collects. For example, the data collection unit can collect detailed data on freelancers who are a good fit for the client's corporate culture. Alternatively, the data collection unit can simplify the collection of data on freelancers who are not a good fit for the client's corporate culture. For example, the data collection unit can assess the suitability of freelancers to the client's corporate culture and prioritize the collection of data on freelancers with a high degree of suitability. This allows the data collection unit to adjust the level of detail of the data it collects by assessing the suitability for the client's corporate culture. Some or all of the above processing in the data collection unit may be performed using AI, for example, or not. For example, the data collection unit can input the suitability for the client's corporate culture into the AI ​​and have the AI ​​perform the adjustment of the level of detail of the data.

[0077] The data collection unit can estimate the freelancer's emotions and select the types of data to collect based on the estimated emotions. For example, if the freelancer is relaxed, the data collection unit can collect detailed skill set and performance data. Alternatively, if the freelancer is stressed, the data collection unit can collect only basic information. For example, if the freelancer is excited, the data collection unit can collect data on their enthusiasm and motivation for the project. This allows the data collection unit to collect more appropriate data by selecting the types of data to collect based on the freelancer's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input the freelancer's emotion data into an AI and have the AI ​​select the types of data to collect.

[0078] The data collection unit can prioritize the collection of highly relevant data by considering the geographical location information of freelancers. For example, if a freelancer lives in a specific region, the data collection unit will prioritize the collection of project data related to that region. The data collection unit can also collect relevant project data by considering the range of movement the freelancer can travel. For example, the data collection unit will collect data tailored to the demand in each region based on the freelancer's geographical location information. In this way, the data collection unit can prioritize the collection of highly relevant data by considering the freelancer's geographical location information. Some or all of the above processing in the data collection unit may be performed using AI, for example, or not using AI. For example, the data collection unit can input the freelancer's geographical location information into AI and have the AI ​​perform the collection of highly relevant data.

[0079] The data collection unit can analyze the social media activities of freelancers and collect relevant data. For example, the data collection unit can analyze the content of a freelancer's social media activities and collect data on their skills and achievements. The data collection unit can also collect data on influence based on the number of followers and engagement rates of freelancers on social media. For example, the data collection unit can analyze the content of a freelancer's social media posts and collect data on their personality and values. In this way, the data collection unit can collect relevant data by analyzing the social media activities of freelancers. Some or all of the above processing in the data collection unit may be performed using AI, for example, or not using AI. For example, the data collection unit can input the freelancer's social media activities into AI and have the AI ​​collect the relevant data.

[0080] The analysis unit can estimate the freelancer's emotions and adjust the analysis method based on the estimated emotions. For example, if the freelancer is relaxed, the analysis unit can perform a detailed analysis to delve deeper into their skills and achievements. Conversely, if the freelancer is stressed, the analysis unit can reduce the burden by performing only a basic analysis. For example, if the freelancer is excited, the analysis unit can focus on their enthusiasm and motivation for the project. This allows the analysis unit to perform a more appropriate analysis by adjusting the analysis method based on the freelancer'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 analysis unit may be performed using AI or not. For example, the analysis unit can input the freelancer's emotion data into an AI and have the AI ​​adjust the analysis method.

[0081] The analysis unit can perform a detailed analysis of freelancers' skill sets and clients' project requirements based on the collected data. For example, the analysis unit can perform a detailed analysis of freelancers' skill sets and make the best match with the client's project requirements. The analysis unit can also perform a detailed analysis of the client's project requirements and identify the necessary skill sets. For example, the analysis unit can compare freelancers' skill sets with the client's project requirements and make the best match. In this way, the analysis unit can make the best match by performing a detailed analysis of freelancers' skill sets and clients' project requirements. Some or all of the above processing in the analysis unit may be performed using AI, for example, or not using AI. For example, the analysis unit can input the collected data into AI and have the AI ​​perform a detailed analysis of skill sets and project requirements.

[0082] The analysis unit can improve the accuracy of its analysis based on the success rate of a freelancer's past projects. For example, the analysis unit can evaluate a freelancer's skill set based on the success rate of their past projects. It can also evaluate a freelancer's suitability for a client's project requirements based on the success rate of their past projects. For example, the analysis unit can improve its algorithms to enhance the accuracy of its analysis based on the success rate of a freelancer's past projects. This allows the analysis unit to perform more accurate analyses by improving the accuracy of its analysis based on the success rate of a freelancer's past projects. Some or all of the above processes in the analysis unit may be performed using AI, or not. For example, the analysis unit can input the success rate of a freelancer's past projects into an AI and have the AI ​​perform the analysis accuracy improvement.

[0083] The analysis unit can estimate the freelancer's emotions and adjust how the analysis results are displayed based on the estimated emotions. For example, if the freelancer is relaxed, the analysis unit can display detailed analysis results. It can also display concise results if the freelancer is stressed. For example, if the freelancer is excited, the analysis unit can display visually stimulating analysis results. This allows the analysis unit to provide more appropriate displays by adjusting how the analysis results are displayed based on the freelancer's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a 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-described processes in the analysis unit may be performed using AI, or not. For example, the analysis unit can input the freelancer's emotion data into an AI and have the AI ​​adjust how the analysis results are displayed.

[0084] The analysis unit can classify the analysis results by region, taking into account the geographical location information of freelancers. For example, the analysis unit can display analysis results tailored to the demand in each region based on the geographical location information of freelancers. The analysis unit can also analyze the trends in skill sets by region based on the geographical location information of freelancers. For example, the analysis unit can analyze the project success rate by region based on the geographical location information of freelancers. In this way, the analysis unit can classify the analysis results by region by taking into account the geographical location information of freelancers. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without using AI. For example, the analysis unit can input the geographical location information of freelancers into AI and have the AI ​​perform the classification of the analysis results by region.

[0085] The analysis unit can analyze the social media activities of freelancers and reflect the results in the analysis. For example, the analysis unit can analyze the content of a freelancer's social media activities and reflect data on their skills and achievements. The analysis unit can also reflect data on their influence based on the number of followers and engagement rate on social media. For example, the analysis unit can analyze the content of a freelancer's social media posts and reflect data on their personality and values. In this way, the analysis unit can analyze the social media activities of freelancers and reflect the results in the analysis. Some or all of the above processing in the analysis unit may be performed using AI, for example, or not using AI. For example, the analysis unit can input the freelancer's social media activities into AI and have AI perform the reflection of the results in the analysis.

[0086] The matching unit can estimate the freelancer's emotions and adjust the matching criteria based on the estimated emotions. For example, if the freelancer is relaxed, the matching unit can apply detailed matching criteria. Conversely, if the freelancer is stressed, the matching unit can apply basic matching criteria. For example, if the freelancer is excited, the matching unit can apply matching criteria that emphasize enthusiasm and motivation for the project. This allows the matching unit to perform more appropriate matching by adjusting the matching criteria based on the freelancer's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the matching unit may be performed using AI or not. For example, the matching unit can input freelancer emotion data into an AI and have the AI ​​adjust the matching criteria.

[0087] The matching unit can perform optimal matching based on the freelancer's skill set and the client's project requirements. For example, the matching unit can perform optimal matching based on the freelancer's skill set and the client's project requirements. The matching unit can also identify freelancers with the necessary skill sets based on the client's project requirements. For example, the matching unit can compare the freelancer's skill set and the client's project requirements to perform optimal matching. In this way, the matching unit can perform more appropriate matching by performing optimal matching based on the freelancer's skill set and the client's project requirements. 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 freelancer's skill set and the client's project requirements into AI and have the AI ​​perform optimal matching.

[0088] The matching unit can improve the accuracy of matching by considering the success rate of a freelancer's past projects. For example, the matching unit evaluates the skill set based on the success rate of a freelancer's past projects. The matching unit can also evaluate the suitability of a freelancer to the client's project requirements based on the success rate of a freelancer's past projects. For example, the matching unit improves the algorithm for improving matching accuracy based on the success rate of a freelancer's past projects. In this way, the matching unit can improve the accuracy of matching by considering the success rate of a freelancer's past projects. 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 success rate of a freelancer's past projects into an AI and have the AI ​​perform the improvement of matching accuracy.

[0089] The matching unit can estimate the freelancer's emotions and adjust the display order of matching results based on the estimated emotions. For example, if a freelancer is relaxed, the matching unit can display detailed matching results. It can also display concise matching results if a freelancer is stressed. For example, if a freelancer is excited, the matching unit can display visually stimulating matching results. This allows the matching unit to provide more appropriate results by adjusting the display order of matching results based on the freelancer'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 freelancer emotion data into an AI and have the AI ​​adjust the display order of matching results.

[0090] The matching unit can perform optimal matching for each region by considering the geographical location information of freelancers. For example, the matching unit can perform matching according to the demand in each region based on the geographical location information of freelancers. The matching unit can also analyze the trends in skill sets in each region based on the geographical location information of freelancers and perform optimal matching. For example, the matching unit can analyze the project success rate in each region based on the geographical location information of freelancers and perform optimal matching. In this way, the matching unit can perform optimal matching for each region by considering the geographical location information of freelancers. Some or all of the above processing in the matching unit may be performed using AI, for example, or not using AI. For example, the matching unit can input the geographical location information of freelancers into AI and have the AI ​​perform optimal matching for each region.

[0091] The matching unit can analyze freelancers' social media activity and reflect it in the matching results. For example, the matching unit can analyze the content of freelancers' social media activities and reflect data on their skills and achievements. The matching unit can also reflect data on influence based on the number of followers and engagement rate of freelancers on social media. For example, the matching unit can analyze the content of freelancers' social media posts and reflect data on their personality and values. In this way, the matching unit can analyze freelancers' social media activity and reflect it in the matching results. Some or all of the above processing in the matching unit may be performed using AI, for example, or not. For example, the matching unit can input freelancers' social media activity into AI and have the AI ​​perform the reflection of it in the matching results.

[0092] The recommendation system can estimate the emotions of freelancers and adjust its recommendation methods based on these estimated emotions. For example, if a freelancer is relaxed, the recommendation system can provide detailed recommendations. If a freelancer is stressed, it can provide concise recommendations. If a freelancer is excited, for example, the recommendation system can provide visually stimulating recommendations. This allows the recommendation system to make more appropriate recommendations by adjusting its methods based on the freelancer's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the recommendation system may be performed using AI or not. For example, the recommendation system can input freelancer emotion data into an AI and have the AI ​​adjust the recommendation methods.

[0093] The recommendation department can improve its recommendation algorithm based on feedback from clients. For example, if a client is satisfied, the recommendation department can improve the recommendation algorithm based on that feedback. Similarly, if a client is dissatisfied, the recommendation department can also improve the recommendation algorithm based on that feedback. For example, the recommendation department can collect feedback from clients and improve the accuracy of the recommendation algorithm. This improves the accuracy of recommendations by improving the recommendation algorithm based on client feedback. Some or all of the above processes in the recommendation department may be performed using AI, or not. For example, the recommendation department can input client feedback into an AI and have the AI ​​improve the recommendation algorithm.

[0094] The recommendation system can improve the accuracy of its recommendations by considering the success rate of a freelancer's past projects. For example, the recommendation system can improve the accuracy of its recommendations based on the success rate of a freelancer's past projects. The recommendation system can also evaluate the suitability of a freelancer to a client's project requirements based on the success rate of a freelancer's past projects. For example, the recommendation system can improve its recommendation algorithm based on the success rate of a freelancer's past projects. This improves the accuracy of the recommendations by considering the success rate of a freelancer's past projects. Some or all of the above processes in the recommendation system may be performed using AI, or not. For example, the recommendation system can input the success rate of a freelancer's past projects into an AI and have the AI ​​perform the recommendation accuracy improvement.

[0095] The recommendation system can estimate the freelancer's emotions and adjust how recommendation results are displayed based on the estimated emotions. For example, if a freelancer is relaxed, the recommendation system can display detailed recommendations. If a freelancer is stressed, it can display concise recommendations. If a freelancer is excited, for example, the recommendation system can display visually stimulating recommendations. This allows the recommendation system to provide more appropriate recommendations by adjusting how recommendations are displayed based on the freelancer's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the recommendation system may be performed using AI or not. For example, the recommendation system can input freelancer emotion data into an AI and have the AI ​​adjust how recommendation results are displayed.

[0096] The recommendation department can make optimal recommendations for each region by taking into account the client's geographical location information. For example, the recommendation department can make recommendations that meet the needs of each region based on the client's geographical location information. Furthermore, the recommendation department can analyze the trends in skill sets for each region based on the client's geographical location information and make optimal recommendations. For example, the recommendation department can analyze the project success rate for each region based on the client's geographical location information and make optimal recommendations. In this way, the recommendation department can make optimal recommendations for each region by considering the client's geographical location information. Some or all of the above processes in the recommendation department may be performed using AI, or not. For example, the recommendation department can input the client's geographical location information into AI and have the AI ​​perform optimal recommendations for each region.

[0097] The recommendation system can analyze a freelancer's social media activity and reflect it in the recommendation results. For example, the recommendation system can analyze a freelancer's social media activity and reflect data on their skills and achievements. It can also reflect data on their influence based on the number of followers and engagement rate on social media. For example, the recommendation system can analyze a freelancer's social media posts and reflect data on their personality and values. In this way, the recommendation system can analyze a freelancer's social media activity and reflect it in the recommendation results. Some or all of the above processing in the recommendation system may be performed using AI, for example, or not. For example, the recommendation system can input a freelancer's social media activity into AI and have the AI ​​perform the reflection of it in the recommendation results.

[0098] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0099] The analysis unit can evaluate a freelancer's growth rate based on the success rate of their past projects. For example, the analysis unit analyzes fluctuations in a freelancer's past project success rate and calculates the growth rate. Furthermore, the analysis unit can evaluate the evolution of a freelancer's skill set and reflect this in the growth rate. For instance, if a freelancer acquires a new skill, the analysis unit reflects its impact in the growth rate. This allows the analysis unit to perform more appropriate matching by evaluating the freelancer's growth rate.

[0100] The data collection unit can estimate the freelancer's emotions and prioritize the data to collect based on that estimate. For example, if the freelancer is relaxed, it will prioritize collecting detailed skill set and performance data. Conversely, if the freelancer is stressed, it may collect only basic information. This allows the data collection unit to collect more relevant data by prioritizing the data to collect based on the freelancer's emotions.

[0101] The matching system can perform optimal matching for each region by considering the geographical location of freelancers. For example, it can perform matching based on the demand in each region based on the geographical location of freelancers. It can also analyze the trends in skill sets in each region based on the geographical location of freelancers and perform optimal matching. In this way, the matching system can perform optimal matching for each region by considering the geographical location of freelancers.

[0102] The recommendation system can estimate the freelancer's emotions and adjust its recommendation methods based on that estimation. For example, if a freelancer is relaxed, it can provide detailed recommendation information. Conversely, if a freelancer is stressed, it can provide concise recommendation information. This allows the recommendation system to make more appropriate recommendations by adjusting its recommendation methods based on the freelancer's emotions.

[0103] The analytics department can analyze freelancers' social media activities and reflect the findings in the analysis results. For example, it can analyze the content of a freelancer's social media activities and reflect data on their skills and achievements. It can also reflect data on their influence based on the number of followers and engagement rate on social media. In this way, the analytics department can analyze freelancers' social media activities and reflect the findings in the analysis results.

[0104] The data collection unit can estimate the freelancer's emotions and adjust the timing of data collection based on the estimated emotions. For example, if a freelancer is stressed, data collection can be temporarily delayed and resumed when they are relaxed. Conversely, if a freelancer is relaxed, data collection can be actively pursued to obtain more detailed information. In this way, the data collection unit can collect data at a more appropriate time by adjusting the timing of data collection based on the freelancer's emotions.

[0105] The matching system can improve the accuracy of matching by considering the success rate of freelancers' past projects. For example, it can evaluate skill sets based on the success rate of freelancers' past projects. It can also evaluate suitability for client project requirements based on the success rate of freelancers' past projects. In this way, the matching system can improve the accuracy of matching by considering the success rate of freelancers' past projects.

[0106] The analysis unit can estimate the freelancer's emotions and adjust the analysis method based on the estimated emotions. For example, if the freelancer is relaxed, it can perform a detailed analysis to delve deeper into their skills and achievements. Conversely, if the freelancer is stressed, it can perform only a basic analysis to reduce the burden. In this way, the analysis unit can perform more appropriate analysis by adjusting the analysis method based on the freelancer's emotions.

[0107] The recommendation department can make optimal recommendations for each region by considering the client's geographical location. For example, it can make recommendations based on the client's geographical location to meet the needs of each region. It can also analyze the trends in skill sets for each region based on the client's geographical location to make optimal recommendations. In this way, the recommendation department can make optimal recommendations for each region by considering the client's geographical location.

[0108] The matching unit can estimate the freelancer's emotions and adjust the display order of matching results based on the estimated emotions. For example, if a freelancer is relaxed, detailed matching results can be displayed. Conversely, if a freelancer is stressed, concise matching results can be displayed. In this way, the matching unit can provide more appropriate results by adjusting the display order of matching results based on the freelancer's emotions.

[0109] The following briefly describes the processing flow for example form 2.

[0110] Step 1: The collection team gathers the freelancer's resume, experience, and personality. For example, they collect information such as the freelancer's work history, skill set, personality traits, success rate of past projects, and suitability for the client's corporate culture. Step 2: The analysis unit analyzes the data collected by the data collection unit to analyze client needs and culture. For example, it analyzes the client's project requirements, corporate values, and work style to conduct a detailed analysis of freelancer and client profiles. Step 3: The matching unit automatically connects the most suitable profiles based on the data analyzed by the analysis unit. For example, it may determine that a freelancer with a specific skill set is best suited for a particular project and connect the most suitable profiles based on skill matching and cultural compatibility. Step 4: The recommendation department proposes projects based on the profiles linked by the matching department, obtains feedback, and makes improvements. For example, it proposes the most suitable freelancer to a client and improves the matching algorithm based on client feedback. It also uses generative AI to improve the accuracy of profile data analysis and performs real-time optimization and scaling.

[0111] 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.

[0112] 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.

[0113] 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.

[0114] Each of the multiple elements described above, including the data collection unit, analysis unit, matching unit, and recommendation unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the data collection unit is implemented by the control unit 46A of the smart device 14 and collects the resumes, achievements, and personalities of freelancers. The analysis unit is implemented by the identification processing unit 290 of the data processing unit 12 and analyzes the collected data to analyze the client's needs and culture. The matching unit is implemented by the identification processing unit 290 of the data processing unit 12 and automatically matches the most suitable profiles based on the analyzed data. The recommendation unit is implemented by the control unit 46A of the smart device 14 and proposes projects, obtains feedback, and makes improvements. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0115] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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).

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.).

[0127] 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.

[0128] 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.

[0129] 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.

[0130] Each of the multiple elements described above, including the data collection unit, analysis unit, matching unit, and recommendation unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the data collection unit is implemented by the control unit 46A of the smart glasses 214 and collects the freelancer's resume, experience, and personality. The analysis unit is implemented by the identification processing unit 290 of the data processing unit 12 and analyzes the collected data to analyze the client's needs and culture. The matching unit is implemented by the identification processing unit 290 of the data processing unit 12 and automatically matches the most suitable profiles based on the analyzed data. The recommendation unit is implemented by the control unit 46A of the smart glasses 214 and proposes projects, obtains feedback, and makes improvements. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0131] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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).

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.).

[0143] 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.

[0144] 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.

[0145] 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.

[0146] Each of the multiple elements described above, including the data collection unit, analysis unit, matching unit, and recommendation unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the data collection unit is implemented by the control unit 46A of the headset terminal 314 and collects the freelancer's resume, experience, and personality. The analysis unit is implemented by the identification processing unit 290 of the data processing unit 12 and analyzes the collected data to analyze the client's needs and culture. The matching unit is implemented by the identification processing unit 290 of the data processing unit 12 and automatically matches the most suitable profiles based on the analyzed data. The recommendation unit is implemented by the control unit 46A of the headset terminal 314 and proposes projects, obtains feedback, and makes improvements. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0147] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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).

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.).

[0160] 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.

[0161] 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.

[0162] 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.

[0163] Each of the multiple elements described above, including the data collection unit, analysis unit, matching unit, and recommendation unit, is implemented in at least one of the robot 414 and the data processing unit 12. For example, the data collection unit is implemented by the control unit 46A of the robot 414 and collects the resumes, achievements, and personalities of freelancers. The analysis unit is implemented by the identification processing unit 290 of the data processing unit 12 and analyzes the collected data to analyze the client's needs and culture. The matching unit is implemented by the identification processing unit 290 of the data processing unit 12 and automatically matches the most suitable profiles based on the analyzed data. The recommendation unit is implemented by the control unit 46A of the robot 414 and proposes projects, obtains feedback, and makes improvements. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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."

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] (Note 1) A collection department that gathers resumes, achievements, and personalities of freelancers, The data collected by the aforementioned collection unit is analyzed by an analysis unit that analyzes the client's needs and culture, Based on the data analyzed by the aforementioned analysis unit, a matching unit automatically connects the most suitable profiles, The system includes a recommendation unit that proposes projects based on profiles linked by the matching unit, obtains feedback, and makes improvements. A system characterized by the following features. (Note 2) The aforementioned collection unit is We collect data such as the success rate of freelancers' past projects and their fit with the client's corporate culture. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned analysis unit, Based on the collected data, we conduct a detailed analysis of freelancer and client profiles. The system described in Appendix 1, characterized by the features described herein. (Note 4) The matching unit is Determining that a freelancer with a specific skill set is best suited for a particular project. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned recommendation department, We propose the most suitable freelancers to our clients and improve our matching algorithm based on client feedback. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned recommendation department, Generative AI is used to improve the accuracy of profile data analysis, enable real-time optimization and scaling, and achieve a personalized touch through next-generation matching algorithms. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned collection unit is We estimate the emotions of freelancers and adjust the timing of data collection based on the estimated emotions of the freelancers. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned collection unit is Analyze the success rates of freelancers' past projects to prioritize the data to collect. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned collection unit is Evaluate the suitability for the client's corporate culture and adjust the level of detail of the data collected. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned collection unit is The system estimates the emotions of freelancers and selects the types of data to collect based on the estimated emotions of the freelancers. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned collection unit is We prioritize collecting highly relevant data, taking into account the geographical location of freelancers. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned collection unit is Analyze the social media activity of freelancers and collect relevant data. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned analysis unit, We estimate the emotions of freelancers and adjust the analysis method based on the estimated emotions of the freelancers. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit, Based on the collected data, we conduct a detailed analysis of freelancers' skill sets and clients' project requirements. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit, Improve the accuracy of the analysis based on the success rate of freelancers' past projects. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit, It estimates the emotions of freelancers and adjusts how the analysis results are displayed based on the estimated emotions of the freelancers. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit, The analysis results are categorized by region, taking into account the geographical location of the freelancers. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned analysis unit, Analyze the social media activity of freelancers and reflect the results in the analysis. The system described in Appendix 1, characterized by the features described herein. (Note 19) The matching unit is We estimate the freelancer's emotions and adjust the matching criteria based on the estimated emotions of the freelancers. The system described in Appendix 1, characterized by the features described herein. (Note 20) The matching unit is We make the best matches based on the freelancer's skill set and the client's project requirements. The system described in Appendix 1, characterized by the features described herein. (Note 21) The matching unit is We improve matching accuracy by considering the success rate of freelancers' past projects. The system described in Appendix 1, characterized by the features described herein. (Note 22) The matching unit is The system estimates the freelancer's emotions and adjusts the display order of matching results based on the estimated emotions of the freelancers. The system described in Appendix 1, characterized by the features described herein. (Note 23) The matching unit is We take into account the geographical location of freelancers to provide the best possible matching for each region. The system described in Appendix 1, characterized by the features described herein. (Note 24) The matching unit is Analyze freelancers' social media activity and reflect it in the matching results. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned recommendation department, We estimate the sentiment of freelancers and adjust the recommendation method based on the estimated sentiment of the freelancers. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned recommendation department, We will improve our recommendation algorithm based on client feedback. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned recommendation department, We improve the accuracy of recommendations by considering the success rate of freelancers' past projects. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned recommendation department, We estimate the freelancer's sentiment and adjust how recommendation results are displayed based on the estimated freelancer's sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned recommendation department, We make the most suitable recommendations for each region, taking into account the client's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned recommendation department, Analyze freelancers' social media activity and reflect it in the recommendation results. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

[0183] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A collection department that gathers resumes, achievements, and personalities of freelancers, The data collected by the aforementioned collection unit is analyzed by an analysis unit that analyzes the client's needs and culture, Based on the data analyzed by the aforementioned analysis unit, a matching unit automatically connects the most suitable profiles, The system includes a recommendation unit that proposes projects based on profiles linked by the matching unit, obtains feedback, and makes improvements. A system characterized by the following features.

2. The aforementioned collection unit is We collect data such as the success rate of freelancers' past projects and their fit with the client's corporate culture. The system according to feature 1.

3. The aforementioned analysis unit, Based on the collected data, we conduct a detailed analysis of freelancer and client profiles. The system according to feature 1.

4. The matching unit is Determining that a freelancer with a specific skill set is best suited for a particular project. The system according to feature 1.

5. The aforementioned recommendation department, We propose the most suitable freelancers to our clients and improve our matching algorithm based on client feedback. The system according to feature 1.

6. The aforementioned recommendation department, Using generative AI, we improve the accuracy of profile data analysis, enable real-time optimization and scaling, and achieve a personalized touch through next-generation matching algorithms. The system according to feature 1.

7. The aforementioned collection unit is We estimate the emotions of freelancers and adjust the timing of data collection based on the estimated emotions of the freelancers. The system according to feature 1.

8. The aforementioned collection unit is Analyze the success rates of freelancers' past projects to prioritize the data to collect. The system according to feature 1.

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