system

Abstract

The system according to this embodiment aims to enable freelancers and sole proprietors to set appropriate prices for their skills and services. [Solution] The system according to the embodiment comprises a collection unit, an analysis unit, and a calculation unit. The collection unit collects information on skills and services provided by freelancers and sole proprietors. The analysis unit analyzes the information collected by the collection unit. The calculation unit calculates an appropriate price based on the information analyzed by the analysis unit.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, there is a problem that it is difficult for freelancers and sole proprietors to set appropriate prices for their skills and services.

[0005] The system according to the embodiment aims to enable freelancers and sole proprietors to set appropriate prices for their skills and services.

Means for Solving the Problems

[0006] The system according to the embodiment includes a collection unit, an analysis unit, and a calculation unit. The collection unit collects information on skills and services provided by freelancers and sole proprietors. The analysis unit analyzes the information collected by the collection unit. The calculation unit calculates an appropriate price based on the information analyzed by the analysis unit. [Effects of the Invention]

[0007] The system according to this embodiment allows freelancers and sole proprietors to set appropriate prices for their skills and services. [Brief explanation of the drawing]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The pricing support system according to an embodiment of the present invention is a system that supports freelancers and sole proprietors in setting prices when providing their skills and services by combining existing talent evaluation systems and talent matching services with AI. The pricing support system collects information on the skills and services provided by freelancers and sole proprietors, and the AI ​​analyzes the collected information to calculate an appropriate price, thereby supporting freelancers and sole proprietors in providing their skills and services at an appropriate price. For example, the pricing support system collects information on the skills and services provided by freelancers and sole proprietors. This information includes internal elements such as specialized knowledge and technical skills, years of experience, project track record, and client satisfaction, as well as external elements such as industry growth potential, market demand, competitive environment, regional economic conditions, and the impact of laws and regulations. For example, it collects information such as the number and scale of projects that the freelancer has successfully completed in the past, and evaluations and feedback from clients. Next, the pricing support system uses AI to analyze the collected information and calculate an appropriate price. Based on the collected internal and external elements, the AI ​​evaluates the value of the skills and services provided by the freelancer and sole proprietor. For example, AI evaluates a freelancer's expertise, technical skills, years of experience, project track record, and client satisfaction, and calculates a fair price considering industry growth potential, market demand, competitive environment, local economic conditions, and the impact of legal regulations. This system allows freelancers and sole proprietors to offer their skills and services at a fair price. For instance, when a freelancer offers their skills or services, they can set a fair price without being dependent on past cases or the client's budget. Companies that commission services can also evaluate the value of the services provided by freelancers and commission them at a fair price. Furthermore, AI helps freelancers to present appropriate prices by considering the competitive environment and the impact of legal regulations. For example, AI analyzes market trends and local economic conditions and provides information to help freelancers set fair prices for the skills and services they offer. This allows freelancers and sole proprietors to set competitive prices and increase their bargaining power with clients.This system supports freelancers and sole proprietors in providing their skills and services at fair prices, and enables companies to request their services at fair prices. This supports the diversification of work styles for freelancers and sole proprietors and improves the cost-effectiveness for companies. Thus, the pricing support system helps freelancers and sole proprietors provide their skills and services at fair prices and improves the cost-effectiveness for companies.

[0029] The pricing support system according to this embodiment comprises a data collection unit, an analysis unit, and a calculation unit. The data collection unit collects information about the skills and services provided by freelancers and sole proprietors. For example, the data collection unit collects internal elements such as the freelancer's or sole proprietor's expertise and technical skills, years of experience, project track record, and client satisfaction. The data collection unit can also collect external elements such as industry growth potential, market demand, competitive environment, regional economic conditions, and the impact of laws and regulations. For example, the data collection unit collects information such as the number and scale of projects that the freelancer has successfully completed in the past, and evaluations and feedback from clients. The analysis unit analyzes the information collected by the data collection unit. For example, the analysis unit evaluates the value of the skills and services provided by the freelancer or sole proprietor based on the collected internal and external elements. For example, the analysis unit evaluates the freelancer's expertise and technical skills, years of experience, project track record, and client satisfaction, and provides a basis for calculating an appropriate price, taking into account industry growth potential, market demand, competitive environment, regional economic conditions, and the impact of laws and regulations. The calculation unit calculates an appropriate price based on the information analyzed by the analysis unit. The calculation unit enables freelancers and sole proprietors to provide services at an appropriate price by, for example, calculating an appropriate price based on the evaluated information. As a result, the pricing support system according to the embodiment can calculate an appropriate price for the skills and services provided by freelancers and sole proprietors. Some or all of the above-described processes in the collection unit, analysis unit, and calculation unit may be performed using AI, for example, or without AI. For example, the collection unit can automatically collect information using AI when collecting information on freelancers and sole proprietors. The analysis unit can analyze the collected information using AI and provide a basis for calculating an appropriate price. The calculation unit can calculate an appropriate price using AI based on the analyzed information. As a result, the pricing support system can calculate an appropriate price for the skills and services provided by freelancers and sole proprietors.

[0030] The data collection department collects information on the skills and services offered by freelancers and sole proprietors. Specifically, it collects internal elements such as the freelancer's expertise and technical skills, years of experience, project track record, and client satisfaction. This information is obtained from data entered by freelancers and sole proprietors when they register, past project history, and client evaluations and feedback. The data collection department can also collect external elements such as industry growth potential, market demand, competitive environment, regional economic conditions, and the impact of laws and regulations. These external elements are obtained from publicly available market research reports, economic data, industry news, and government statistics. For example, the data collection department collects information such as the number and scale of successful projects a freelancer has completed in the past, and client evaluations and feedback. This allows the data collection department to comprehensively collect detailed information on the skills and services offered by freelancers and sole proprietors and prepare foundational data to provide to the analysis and calculation departments. Furthermore, the data collection department can centrally manage this data and link it with other systems and departments as needed. For example, collected data is stored on a cloud server, making it accessible to the analysis and calculation units. Furthermore, by adjusting the frequency and accuracy of data collection, flexible responses to specific situations and conditions become possible. This allows the data collection unit to collect data efficiently and effectively, improving the overall system performance.

[0031] The analysis unit analyzes the information collected by the data collection unit. Specifically, it evaluates the value of the skills and services provided by freelancers and sole proprietors based on the collected internal and external factors. For example, the analysis unit evaluates freelancers' expertise, technical skills, years of experience, project track record, and client satisfaction, and provides a basis for calculating appropriate prices by considering industry growth potential, market demand, competitive environment, regional economic conditions, and the impact of laws and regulations. The analysis unit uses AI to analyze this data and comprehensively evaluate multiple factors to perform a more accurate value assessment. For example, the AI ​​evaluates freelancers' skill levels and reliability based on past project data and client evaluations, and provides basic data for calculating appropriate prices by considering industry trends and market demand. The analysis unit can also utilize past data and statistical information to perform long-term risk assessments and trend analyses. For example, based on past project data, it can predict fluctuations in demand for specific skills or services and use this as a reference for future pricing. Furthermore, the analysis unit can use anomaly detection algorithms to detect unusual patterns and abnormal data, and issue warnings early. This allows the analysis unit to not only grasp the situation in real time, but also to handle long-term risk management and anomaly detection, thereby improving the reliability and safety of the entire system.

[0032] The calculation unit calculates a fair price based on the information analyzed by the analysis unit. Specifically, by calculating a fair price based on the evaluated information, it enables freelancers and sole proprietors to provide services at a fair price. The calculation unit uses AI to analyze this data and calculates a more accurate price by comprehensively evaluating multiple factors. For example, the AI ​​evaluates the freelancer's skill level and reliability based on past project data and client evaluations, and calculates a fair price considering industry trends and market demand. Furthermore, the calculation unit can continuously adjust pricing based on real-time updated data to respond to the latest situations. For example, if market demand or the competitive environment changes, the calculation unit immediately incorporates new data and updates the pricing. The calculation unit can also perform more accurate pricing by considering regional characteristics and past pricing history. As a result, the calculation unit can always provide highly accurate pricing based on the latest information, supporting freelancers and sole proprietors in providing services at a fair price. In addition, the calculation unit can collect user feedback and continuously improve the accuracy and effectiveness of pricing. For example, the pricing algorithm can be revised based on user and market reactions to pricing, allowing for the calculation of more appropriate prices. This enables the calculation unit to determine fair prices for skills and services offered by freelancers and sole proprietors, thereby improving the overall system performance.

[0033] The data collection unit can collect internal elements of freelancers and sole proprietors, such as their expertise, technical skills, years of experience, project track record, and client satisfaction. For example, the data collection unit can collect the expertise and technical skills of freelancers and sole proprietors. For example, it can collect qualifications, work experience, and technical test results. The data collection unit can also collect the years of experience of freelancers and sole proprietors. For example, it can collect the number of projects and years of service in the industry. The data collection unit can also collect the project track record of freelancers and sole proprietors. For example, it can collect project size, success rate, and customer feedback. The data collection unit can also collect the client satisfaction of freelancers and sole proprietors. For example, it can collect survey results, repeat customer rates, and word-of-mouth ratings. In this way, the data collection unit can obtain information to calculate appropriate pricing by collecting the internal elements of freelancers and sole proprietors. 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 use AI to automatically collect information on freelancers and sole proprietors.

[0034] The data collection unit can collect external factors such as industry growth potential, market demand, competitive environment, regional economic conditions, and the impact of laws and regulations. For example, the data collection unit can collect industry growth potential. For example, it can collect market research data and industry growth rate forecasts. The data collection unit can also collect market demand. For example, it can collect demand forecast data and consumer survey results. The data collection unit can also collect the competitive environment. For example, it can collect the number of competitors, market share, and competitive strategies. The data collection unit can also collect regional economic conditions. For example, it can collect regional economic indicators, unemployment rates, and income levels. The data collection unit can also collect the impact of laws and regulations. For example, it can collect the content and compliance status of relevant laws and regulations. By collecting these external factors, the data collection unit can obtain information for calculating appropriate prices. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can use AI to automatically collect information such as industry growth potential, market demand, competitive environment, regional economic conditions, and the impact of laws and regulations.

[0035] The analysis unit can evaluate the value of skills and services provided by freelancers and sole proprietors based on collected internal and external factors. For example, the analysis unit can evaluate the value of skills and services provided by freelancers and sole proprietors using cost-benefit analysis, customer satisfaction surveys, competitive analysis, etc. This allows the analysis unit to provide a basis for calculating appropriate prices by evaluating the value of skills and services based on internal and external factors. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can analyze the collected information using AI to evaluate the value of skills and services provided by freelancers and sole proprietors.

[0036] The calculation unit can calculate a fair price based on the evaluated information. For example, the calculation unit can calculate a fair price using criteria such as market price, cost-based pricing, or competitive pricing. This allows the calculation unit to enable freelancers and sole proprietors to provide services at a fair price by calculating a fair price based on the evaluated information. Some or all of the above-described processes in the calculation unit may be performed using AI, for example, or not using AI. For example, the calculation unit can analyze the evaluated information using AI and calculate a fair price.

[0037] The analysis unit can help freelancers set appropriate prices by considering the competitive environment and the impact of legal regulations. For example, the analysis unit can help freelancers set appropriate prices by considering the competitive environment and the impact of legal regulations. For example, the analysis unit can evaluate the number of competitors, market share, and competitive strategies to enable freelancers to set competitive prices. The analysis unit can also evaluate the content and compliance status of relevant laws and regulations to enable freelancers to set prices that comply with legal regulations. In this way, the analysis unit can help freelancers set appropriate prices by considering the competitive environment and the impact of legal regulations. 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 use AI to analyze the competitive environment and the impact of legal regulations to help freelancers set appropriate prices.

[0038] The data collection unit can analyze the past project history of freelancers and sole proprietors and select the optimal information collection method. For example, the data collection unit can analyze the success rate of past projects and prioritize the collection of information related to successful projects. For example, the data collection unit can collect information on similar projects based on the scale and content of past projects. For example, the data collection unit can analyze client feedback on past projects and collect information related to highly-rated projects. In this way, the data collection unit can select the optimal information collection method by analyzing past project history. 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 use AI to analyze the past project history of freelancers and sole proprietors and select the optimal information collection method.

[0039] The data collection unit can filter information based on the user's current projects and areas of interest during data collection. For example, the data collection unit prioritizes collecting information related to the user's current projects. For example, the data collection unit filters and provides relevant information based on the user's areas of interest. For example, the data collection unit collects and provides information on areas the user has shown interest in in the past. In this way, the data collection unit can provide highly relevant information by filtering information based on the user's current projects and areas of interest. 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 use AI to automatically filter information when filtering information based on the user's current projects and areas of interest.

[0040] The data collection unit can prioritize the collection of highly relevant information by considering the user's geographical location information during data collection. For example, the data collection unit can prioritize the collection of information related to the area where the user is currently located. For example, the data collection unit can collect and provide information about areas the user has visited in the past. For example, the data collection unit can collect and provide information about areas the user plans to visit in the future. In this way, the data collection unit can provide highly relevant information by considering geographical location information. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can analyze the user's geographical location information using AI and prioritize the collection of highly relevant information.

[0041] The data collection unit can analyze a user's social media activity and collect relevant information during data collection. For example, the data collection unit can collect relevant information based on information shared by the user on social media. For example, the data collection unit can analyze the content of posts from accounts the user follows and collect relevant information. For example, the data collection unit can analyze the activities of groups and communities the user participates in and collect relevant information. In this way, the data collection unit can provide relevant information by analyzing social media activity. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can analyze a user's social media activity using AI and collect relevant information.

[0042] The analysis unit can adjust the level of detail of the analysis based on the importance of the collected information during the analysis. For example, the analysis unit performs a detailed analysis on information of high importance. For example, the analysis unit performs a simplified analysis on information of low importance. For example, the analysis unit determines the priority of the analysis according to the importance of the information. This enables efficient analysis by adjusting the level of detail of the analysis based on the importance of the information. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can use AI to evaluate the importance of the collected information and adjust the level of detail of the analysis.

[0043] The analysis unit can apply different analysis algorithms depending on the category of information during analysis. For example, the analysis unit applies a specialized analysis algorithm to information about technical skills. For example, the analysis unit applies an evaluation algorithm to information about client satisfaction. For example, the analysis unit applies a trend analysis algorithm to information about market demand. In this way, the analysis unit can provide highly accurate analysis results by applying an appropriate analysis algorithm according to the category of information. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can apply different analysis algorithms using AI depending on the category of information.

[0044] The analysis unit can determine the priority of analysis based on the timing of information collection during the analysis. For example, the analysis unit prioritizes the analysis of the latest information. For example, the analysis unit lowers the priority of analysis of older information. For example, the analysis unit adjusts the level of detail of the analysis according to the timing of information collection. In this way, the analysis unit can prioritize the analysis of the latest information by determining the priority of analysis based on the timing of information collection. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can use AI to evaluate the timing of information collection and determine the priority of analysis.

[0045] The analysis unit can adjust the order of analysis based on the relevance of the information during the analysis. For example, the analysis unit prioritizes the analysis of highly relevant information. For example, the analysis unit postpones the analysis of less relevant information. For example, the analysis unit adjusts the level of detail of the analysis according to the relevance of the information. In this way, the analysis unit can perform efficient analysis by adjusting the order of analysis based on the relevance of the information. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can use AI to evaluate the relevance of the information and adjust the order of analysis.

[0046] The calculation unit can improve the accuracy of its calculations by considering the interrelationships of information during the calculation process. For example, the calculation unit calculates the price by considering the interrelationship between expertise and technical skills. For example, the calculation unit calculates the price by considering the interrelationship between project performance and client satisfaction. For example, the calculation unit calculates the price by considering the interrelationship between market demand and the competitive environment. As a result, the calculation unit can calculate prices with high accuracy by considering the interrelationships of information. Some or all of the above-described processes in the calculation unit may be performed using AI, for example, or without AI. For example, the calculation unit can use AI to evaluate the interrelationships of information and improve the accuracy of its calculations.

[0047] The calculation unit can perform calculations while considering the attribute information of the information submitter. For example, the calculation unit can calculate the price by considering the submitter's expertise and technical skills. For example, the calculation unit can calculate the price by considering the submitter's years of experience and project track record. For example, the calculation unit can calculate the price by considering the submitter's client satisfaction. In this way, the calculation unit can calculate an appropriate price by considering the submitter's attribute information. Some or all of the above processing in the calculation unit may be performed using AI, for example, or without AI. For example, the calculation unit can evaluate the submitter's attribute information using AI and perform calculations.

[0048] The calculation unit can perform calculations while considering the geographical distribution of information. For example, the calculation unit can calculate prices while considering the regional economic conditions. For example, the calculation unit can calculate prices while considering the regional competitive environment. For example, the calculation unit can calculate prices while considering regional market demand. In this way, the calculation unit can calculate appropriate prices according to the region by considering the geographical distribution. Some or all of the above processing in the calculation unit may be performed using AI, for example, or without using AI. For example, the calculation unit can evaluate the geographical distribution of information using AI and then perform calculations.

[0049] The calculation unit can improve the accuracy of its calculations by referring to relevant literature during the calculation process. For example, the calculation unit calculates prices by referring to relevant academic papers. For example, the calculation unit calculates prices by referring to relevant industry reports. For example, the calculation unit calculates prices by referring to relevant market research data. This enables the calculation unit to calculate prices with high accuracy by referring to relevant literature. Some or all of the above processes in the calculation unit may be performed using AI, for example, or without AI. For example, the calculation unit can use AI to evaluate relevant literature and improve the accuracy of its calculations.

[0050] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0051] The data collection unit can prioritize the collection of highly relevant information by considering the user's geographical location. For example, it can prioritize the collection of information related to the area the user is currently in. It can also collect and provide information about areas the user has visited in the past. Furthermore, it can collect and provide information about areas the user plans to visit in the future. In this way, the data collection unit can provide highly relevant information by considering geographical location. 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 analyze the user's geographical location using AI and prioritize the collection of highly relevant information.

[0052] The analysis unit can apply different analysis algorithms depending on the category of information. For example, a specialized analysis algorithm is applied to information about technical skills. An evaluation algorithm is applied to information about client satisfaction. A trend analysis algorithm is applied to information about market demand. In this way, the analysis unit can provide highly accurate analysis results by applying the appropriate analysis algorithm according to the category of information. Some or all of the above processing in the analysis unit may be performed using AI or not. For example, the analysis unit can apply different analysis algorithms using AI depending on the category of information.

[0053] The calculation unit can improve the accuracy of its calculations by considering the interrelationships of information. For example, it can calculate prices by considering the interrelationship between specialized knowledge and technical skills. It can calculate prices by considering the interrelationship between project performance and client satisfaction. It can calculate prices by considering the interrelationship between market demand and the competitive environment. In this way, the calculation unit can calculate prices with high accuracy by considering the interrelationships of information. Some or all of the above processing in the calculation unit may be performed using AI or not. For example, the calculation unit can use AI to evaluate the interrelationships of information and improve the accuracy of its calculations.

[0054] The data collection unit can analyze a user's social media activity and collect relevant information. For example, it can collect relevant information based on information shared by the user on social media. It can also analyze the content of posts from accounts the user follows and collect relevant information. It can analyze the activities of groups and communities the user participates in and collect relevant information. In this way, the data collection unit can provide relevant information by analyzing social media activity. 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 use AI to analyze a user's social media activity and collect relevant information.

[0055] The analysis unit can determine the priority of analysis based on when the information was collected. For example, it can prioritize the analysis of the latest information and lower the priority of analysis of older information. It can also adjust the level of detail of the analysis according to when the information was collected. In this way, the analysis unit can prioritize the analysis of the latest information by determining the priority of analysis based on when the information was collected. Some or all of the above processing in the analysis unit may be performed using AI or not. For example, the analysis unit can use AI to evaluate when the information was collected and determine the priority of analysis.

[0056] The following briefly describes the processing flow for example form 1.

[0057] Step 1: The data collection department gathers information about the skills and services offered by freelancers and sole proprietors. For example, the data collection department collects internal factors such as the freelancer's expertise and technical skills, years of experience, project track record, and client satisfaction. The data collection department can also collect external factors such as industry growth potential, market demand, competitive environment, local economic conditions, and the impact of laws and regulations. For example, the data collection department collects information such as the number and scale of successful projects the freelancer has completed in the past, as well as client ratings and feedback. Step 2: The analysis unit analyzes the information collected by the collection unit. For example, the analysis unit evaluates the value of the skills and services provided by freelancers and sole proprietors based on the collected internal and external factors. For instance, the analysis unit evaluates the freelancer's expertise and technical skills, years of experience, project track record, and client satisfaction, and provides a basis for calculating a fair price by considering industry growth potential, market demand, competitive environment, local economic conditions, and the impact of laws and regulations. Step 3: The calculation unit calculates a fair price based on the information analyzed by the analysis unit. For example, the calculation unit calculates a fair price based on the evaluated information, enabling freelancers and sole proprietors to provide services at a fair price.

[0058] (Example of form 2) The pricing support system according to an embodiment of the present invention is a system that supports freelancers and sole proprietors in setting prices when providing their skills and services by combining existing talent evaluation systems and talent matching services with AI. The pricing support system collects information on the skills and services provided by freelancers and sole proprietors, and the AI ​​analyzes the collected information to calculate an appropriate price, thereby supporting freelancers and sole proprietors in providing their skills and services at an appropriate price. For example, the pricing support system collects information on the skills and services provided by freelancers and sole proprietors. This information includes internal elements such as specialized knowledge and technical skills, years of experience, project track record, and client satisfaction, as well as external elements such as industry growth potential, market demand, competitive environment, regional economic conditions, and the impact of laws and regulations. For example, it collects information such as the number and scale of projects that the freelancer has successfully completed in the past, and evaluations and feedback from clients. Next, the pricing support system uses AI to analyze the collected information and calculate an appropriate price. Based on the collected internal and external elements, the AI ​​evaluates the value of the skills and services provided by the freelancer and sole proprietor. For example, AI evaluates a freelancer's expertise, technical skills, years of experience, project track record, and client satisfaction, and calculates a fair price considering industry growth potential, market demand, competitive environment, local economic conditions, and the impact of legal regulations. This system allows freelancers and sole proprietors to offer their skills and services at a fair price. For instance, when a freelancer offers their skills or services, they can set a fair price without being dependent on past cases or the client's budget. Companies that commission services can also evaluate the value of the services provided by freelancers and commission them at a fair price. Furthermore, AI helps freelancers to present appropriate prices by considering the competitive environment and the impact of legal regulations. For example, AI analyzes market trends and local economic conditions and provides information to help freelancers set fair prices for the skills and services they offer. This allows freelancers and sole proprietors to set competitive prices and increase their bargaining power with clients.This system supports freelancers and sole proprietors in providing their skills and services at fair prices, and enables companies to request their services at fair prices. This supports the diversification of work styles for freelancers and sole proprietors and improves the cost-effectiveness for companies. Thus, the pricing support system helps freelancers and sole proprietors provide their skills and services at fair prices and improves the cost-effectiveness for companies.

[0059] The pricing support system according to this embodiment comprises a data collection unit, an analysis unit, and a calculation unit. The data collection unit collects information about the skills and services provided by freelancers and sole proprietors. For example, the data collection unit collects internal elements such as the freelancer's or sole proprietor's expertise and technical skills, years of experience, project track record, and client satisfaction. The data collection unit can also collect external elements such as industry growth potential, market demand, competitive environment, regional economic conditions, and the impact of laws and regulations. For example, the data collection unit collects information such as the number and scale of projects that the freelancer has successfully completed in the past, and evaluations and feedback from clients. The analysis unit analyzes the information collected by the data collection unit. For example, the analysis unit evaluates the value of the skills and services provided by the freelancer or sole proprietor based on the collected internal and external elements. For example, the analysis unit evaluates the freelancer's expertise and technical skills, years of experience, project track record, and client satisfaction, and provides a basis for calculating an appropriate price, taking into account industry growth potential, market demand, competitive environment, regional economic conditions, and the impact of laws and regulations. The calculation unit calculates an appropriate price based on the information analyzed by the analysis unit. The calculation unit enables freelancers and sole proprietors to provide services at an appropriate price by, for example, calculating an appropriate price based on the evaluated information. As a result, the pricing support system according to the embodiment can calculate an appropriate price for the skills and services provided by freelancers and sole proprietors. Some or all of the above-described processes in the collection unit, analysis unit, and calculation unit may be performed using AI, for example, or without AI. For example, the collection unit can automatically collect information using AI when collecting information on freelancers and sole proprietors. The analysis unit can analyze the collected information using AI and provide a basis for calculating an appropriate price. The calculation unit can calculate an appropriate price using AI based on the analyzed information. As a result, the pricing support system can calculate an appropriate price for the skills and services provided by freelancers and sole proprietors.

[0060] The data collection department collects information on the skills and services offered by freelancers and sole proprietors. Specifically, it collects internal elements such as the freelancer's expertise and technical skills, years of experience, project track record, and client satisfaction. This information is obtained from data entered by freelancers and sole proprietors when they register, past project history, and client evaluations and feedback. The data collection department can also collect external elements such as industry growth potential, market demand, competitive environment, regional economic conditions, and the impact of laws and regulations. These external elements are obtained from publicly available market research reports, economic data, industry news, and government statistics. For example, the data collection department collects information such as the number and scale of successful projects a freelancer has completed in the past, and client evaluations and feedback. This allows the data collection department to comprehensively collect detailed information on the skills and services offered by freelancers and sole proprietors and prepare foundational data to provide to the analysis and calculation departments. Furthermore, the data collection department can centrally manage this data and link it with other systems and departments as needed. For example, collected data is stored on a cloud server, making it accessible to the analysis and calculation units. Furthermore, by adjusting the frequency and accuracy of data collection, flexible responses to specific situations and conditions become possible. This allows the data collection unit to collect data efficiently and effectively, improving the overall system performance.

[0061] The analysis unit analyzes the information collected by the data collection unit. Specifically, it evaluates the value of the skills and services provided by freelancers and sole proprietors based on the collected internal and external factors. For example, the analysis unit evaluates freelancers' expertise, technical skills, years of experience, project track record, and client satisfaction, and provides a basis for calculating appropriate prices by considering industry growth potential, market demand, competitive environment, regional economic conditions, and the impact of laws and regulations. The analysis unit uses AI to analyze this data and comprehensively evaluate multiple factors to perform a more accurate value assessment. For example, the AI ​​evaluates freelancers' skill levels and reliability based on past project data and client evaluations, and provides basic data for calculating appropriate prices by considering industry trends and market demand. The analysis unit can also utilize past data and statistical information to perform long-term risk assessments and trend analyses. For example, based on past project data, it can predict fluctuations in demand for specific skills or services and use this as a reference for future pricing. Furthermore, the analysis unit can use anomaly detection algorithms to detect unusual patterns and abnormal data, and issue warnings early. This allows the analysis unit to not only grasp the situation in real time, but also to handle long-term risk management and anomaly detection, thereby improving the reliability and safety of the entire system.

[0062] The calculation unit calculates a fair price based on the information analyzed by the analysis unit. Specifically, by calculating a fair price based on the evaluated information, it enables freelancers and sole proprietors to provide services at a fair price. The calculation unit uses AI to analyze this data and calculates a more accurate price by comprehensively evaluating multiple factors. For example, the AI ​​evaluates the freelancer's skill level and reliability based on past project data and client evaluations, and calculates a fair price considering industry trends and market demand. Furthermore, the calculation unit can continuously adjust pricing based on real-time updated data to respond to the latest situations. For example, if market demand or the competitive environment changes, the calculation unit immediately incorporates new data and updates the pricing. The calculation unit can also perform more accurate pricing by considering regional characteristics and past pricing history. As a result, the calculation unit can always provide highly accurate pricing based on the latest information, supporting freelancers and sole proprietors in providing services at a fair price. In addition, the calculation unit can collect user feedback and continuously improve the accuracy and effectiveness of pricing. For example, the pricing algorithm can be revised based on user and market reactions to pricing, allowing for the calculation of more appropriate prices. This enables the calculation unit to determine fair prices for skills and services offered by freelancers and sole proprietors, thereby improving the overall system performance.

[0063] The data collection unit can collect internal elements of freelancers and sole proprietors, such as their expertise, technical skills, years of experience, project track record, and client satisfaction. For example, the data collection unit can collect the expertise and technical skills of freelancers and sole proprietors. For example, it can collect qualifications, work experience, and technical test results. The data collection unit can also collect the years of experience of freelancers and sole proprietors. For example, it can collect the number of projects and years of service in the industry. The data collection unit can also collect the project track record of freelancers and sole proprietors. For example, it can collect project size, success rate, and customer feedback. The data collection unit can also collect the client satisfaction of freelancers and sole proprietors. For example, it can collect survey results, repeat customer rates, and word-of-mouth ratings. In this way, the data collection unit can obtain information to calculate appropriate pricing by collecting the internal elements of freelancers and sole proprietors. 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 use AI to automatically collect information on freelancers and sole proprietors.

[0064] The data collection unit can collect external factors such as industry growth potential, market demand, competitive environment, regional economic conditions, and the impact of laws and regulations. For example, the data collection unit can collect industry growth potential. For example, it can collect market research data and industry growth rate forecasts. The data collection unit can also collect market demand. For example, it can collect demand forecast data and consumer survey results. The data collection unit can also collect the competitive environment. For example, it can collect the number of competitors, market share, and competitive strategies. The data collection unit can also collect regional economic conditions. For example, it can collect regional economic indicators, unemployment rates, and income levels. The data collection unit can also collect the impact of laws and regulations. For example, it can collect the content and compliance status of relevant laws and regulations. By collecting these external factors, the data collection unit can obtain information for calculating appropriate prices. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can use AI to automatically collect information such as industry growth potential, market demand, competitive environment, regional economic conditions, and the impact of laws and regulations.

[0065] The analysis unit can evaluate the value of skills and services provided by freelancers and sole proprietors based on collected internal and external factors. For example, the analysis unit can evaluate the value of skills and services provided by freelancers and sole proprietors using cost-benefit analysis, customer satisfaction surveys, competitive analysis, etc. This allows the analysis unit to provide a basis for calculating appropriate prices by evaluating the value of skills and services based on internal and external factors. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can analyze the collected information using AI to evaluate the value of skills and services provided by freelancers and sole proprietors.

[0066] The calculation unit can calculate a fair price based on the evaluated information. For example, the calculation unit can calculate a fair price using criteria such as market price, cost-based pricing, or competitive pricing. This allows the calculation unit to enable freelancers and sole proprietors to provide services at a fair price by calculating a fair price based on the evaluated information. Some or all of the above-described processes in the calculation unit may be performed using AI, for example, or not using AI. For example, the calculation unit can analyze the evaluated information using AI and calculate a fair price.

[0067] The analysis unit can help freelancers set appropriate prices by considering the competitive environment and the impact of legal regulations. For example, the analysis unit can help freelancers set appropriate prices by considering the competitive environment and the impact of legal regulations. For example, the analysis unit can evaluate the number of competitors, market share, and competitive strategies to enable freelancers to set competitive prices. The analysis unit can also evaluate the content and compliance status of relevant laws and regulations to enable freelancers to set prices that comply with legal regulations. In this way, the analysis unit can help freelancers set appropriate prices by considering the competitive environment and the impact of legal regulations. 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 use AI to analyze the competitive environment and the impact of legal regulations to help freelancers set appropriate prices.

[0068] The data collection unit can estimate the user's emotions and adjust the timing of information collection based on the estimated emotions. For example, if the user is stressed, the data collection unit can reduce the frequency of information collection and collect information when the user is relaxed. For example, if the user is concentrating, the data collection unit can adjust the timing of information collection to avoid interrupting their work. For example, if the user is in a hurry, the data collection unit can collect information quickly and provide the necessary information immediately. In this way, the data collection unit can reduce the burden on the user by adjusting the timing of information collection based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI, for example, or not using AI. For example, the data collection unit can estimate the user's emotions using AI and adjust the timing of information collection.

[0069] The data collection unit can analyze the past project history of freelancers and sole proprietors and select the optimal information collection method. For example, the data collection unit can analyze the success rate of past projects and prioritize the collection of information related to successful projects. For example, the data collection unit can collect information on similar projects based on the scale and content of past projects. For example, the data collection unit can analyze client feedback on past projects and collect information related to highly-rated projects. In this way, the data collection unit can select the optimal information collection method by analyzing past project history. 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 use AI to analyze the past project history of freelancers and sole proprietors and select the optimal information collection method.

[0070] The data collection unit can filter information based on the user's current projects and areas of interest during data collection. For example, the data collection unit prioritizes collecting information related to the user's current projects. For example, the data collection unit filters and provides relevant information based on the user's areas of interest. For example, the data collection unit collects and provides information on areas the user has shown interest in in the past. In this way, the data collection unit can provide highly relevant information by filtering information based on the user's current projects and areas of interest. 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 use AI to automatically filter information when filtering information based on the user's current projects and areas of interest.

[0071] The data collection unit can estimate the user's emotions and determine the priority of information to collect based on the estimated emotions. For example, if the user is stressed, the data collection unit prioritizes and quickly provides high-priority information. For example, if the user is relaxed, the data collection unit collects and provides detailed information. For example, if the user is in a hurry, the data collection unit quickly collects and provides only the essential information. In this way, the data collection unit can quickly provide important information by prioritizing information based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI, for example, or not using AI. For example, the data collection unit can estimate the user's emotions using AI and determine the priority of information to collect.

[0072] The data collection unit can prioritize the collection of highly relevant information by considering the user's geographical location information during data collection. For example, the data collection unit can prioritize the collection of information related to the area where the user is currently located. For example, the data collection unit can collect and provide information about areas the user has visited in the past. For example, the data collection unit can collect and provide information about areas the user plans to visit in the future. In this way, the data collection unit can provide highly relevant information by considering geographical location information. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can analyze the user's geographical location information using AI and prioritize the collection of highly relevant information.

[0073] The data collection unit can analyze a user's social media activity and collect relevant information during data collection. For example, the data collection unit can collect relevant information based on information shared by the user on social media. For example, the data collection unit can analyze the content of posts from accounts the user follows and collect relevant information. For example, the data collection unit can analyze the activities of groups and communities the user participates in and collect relevant information. In this way, the data collection unit can provide relevant information by analyzing social media activity. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can analyze a user's social media activity using AI and collect relevant information.

[0074] The analysis unit can estimate the user's emotions and adjust the presentation of the analysis based on the estimated emotions. For example, if the user is tense, the analysis unit provides simple and easy-to-understand analysis results. For example, if the user is relaxed, the analysis unit provides detailed analysis results. For example, if the user is in a hurry, the analysis unit provides concise analysis results. In this way, the analysis unit can provide analysis results that are easy for the user to understand by adjusting the presentation of the analysis based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can estimate the user's emotions using AI and adjust the presentation of the analysis.

[0075] The analysis unit can adjust the level of detail of the analysis based on the importance of the collected information during the analysis. For example, the analysis unit performs a detailed analysis on information of high importance. For example, the analysis unit performs a simplified analysis on information of low importance. For example, the analysis unit determines the priority of the analysis according to the importance of the information. This enables efficient analysis by adjusting the level of detail of the analysis based on the importance of the information. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can use AI to evaluate the importance of the collected information and adjust the level of detail of the analysis.

[0076] The analysis unit can apply different analysis algorithms depending on the category of information during analysis. For example, the analysis unit applies a specialized analysis algorithm to information about technical skills. For example, the analysis unit applies an evaluation algorithm to information about client satisfaction. For example, the analysis unit applies a trend analysis algorithm to information about market demand. In this way, the analysis unit can provide highly accurate analysis results by applying an appropriate analysis algorithm according to the category of information. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can apply different analysis algorithms using AI depending on the category of information.

[0077] The analysis unit can estimate the user's emotions and adjust the length of the analysis based on the estimated emotions. For example, if the user is in a hurry, the analysis unit provides a short, concise analysis result. For example, if the user is relaxed, the analysis unit provides a detailed analysis result. For example, if the user is excited, the analysis unit provides a visually stimulating analysis result. In this way, the analysis unit can provide an analysis result of an appropriate length for the user by adjusting the length of the analysis based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can estimate the user's emotions using AI and adjust the length of the analysis.

[0078] The analysis unit can determine the priority of analysis based on the timing of information collection during the analysis. For example, the analysis unit prioritizes the analysis of the latest information. For example, the analysis unit lowers the priority of analysis of older information. For example, the analysis unit adjusts the level of detail of the analysis according to the timing of information collection. In this way, the analysis unit can prioritize the analysis of the latest information by determining the priority of analysis based on the timing of information collection. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can use AI to evaluate the timing of information collection and determine the priority of analysis.

[0079] The analysis unit can adjust the order of analysis based on the relevance of the information during the analysis. For example, the analysis unit prioritizes the analysis of highly relevant information. For example, the analysis unit postpones the analysis of less relevant information. For example, the analysis unit adjusts the level of detail of the analysis according to the relevance of the information. In this way, the analysis unit can perform efficient analysis by adjusting the order of analysis based on the relevance of the information. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can use AI to evaluate the relevance of the information and adjust the order of analysis.

[0080] The calculation unit can estimate the user's emotions and determine the priority of prices to calculate based on the estimated emotions. For example, if the user is stressed, the calculation unit can quickly calculate and provide a price. For example, if the user is relaxed, the calculation unit can perform a detailed price calculation and provide a price. For example, if the user is in a hurry, the calculation unit can calculate and provide a price based on the minimum necessary information. In this way, the calculation unit can provide a quick and appropriate price by determining the priority of prices based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the calculation unit may be performed using AI or not using AI. For example, the calculation unit can estimate the user's emotions using AI and determine the priority of prices to calculate.

[0081] The calculation unit can improve the accuracy of its calculations by considering the interrelationships of information during the calculation process. For example, the calculation unit calculates the price by considering the interrelationship between expertise and technical skills. For example, the calculation unit calculates the price by considering the interrelationship between project performance and client satisfaction. For example, the calculation unit calculates the price by considering the interrelationship between market demand and the competitive environment. As a result, the calculation unit can calculate prices with high accuracy by considering the interrelationships of information. Some or all of the above-described processes in the calculation unit may be performed using AI, for example, or without AI. For example, the calculation unit can use AI to evaluate the interrelationships of information and improve the accuracy of its calculations.

[0082] The calculation unit can perform calculations while considering the attribute information of the information submitter. For example, the calculation unit can calculate the price by considering the submitter's expertise and technical skills. For example, the calculation unit can calculate the price by considering the submitter's years of experience and project track record. For example, the calculation unit can calculate the price by considering the submitter's client satisfaction. In this way, the calculation unit can calculate an appropriate price by considering the submitter's attribute information. Some or all of the above processing in the calculation unit may be performed using AI, for example, or without AI. For example, the calculation unit can evaluate the submitter's attribute information using AI and perform calculations.

[0083] The calculation unit can estimate the user's emotions and adjust the display method of the calculated price based on the estimated user emotions. For example, if the user is nervous, the calculation unit provides a simple and highly visible display method. For example, if the user is relaxed, the calculation unit provides a display method that includes detailed information. For example, if the user is in a hurry, the calculation unit provides a display method that gets straight to the point. In this way, by adjusting the price display method based on the user's emotions, the calculation unit can provide a display that is easy for the user to understand. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the calculation unit may be performed using AI, for example, or not using AI. For example, the calculation unit can estimate the user's emotions using AI and adjust the display method of the calculated price.

[0084] The calculation unit can perform calculations while considering the geographical distribution of information. For example, the calculation unit can calculate prices while considering the regional economic conditions. For example, the calculation unit can calculate prices while considering the regional competitive environment. For example, the calculation unit can calculate prices while considering regional market demand. In this way, the calculation unit can calculate appropriate prices according to the region by considering the geographical distribution. Some or all of the above processing in the calculation unit may be performed using AI, for example, or without using AI. For example, the calculation unit can evaluate the geographical distribution of information using AI and then perform calculations.

[0085] The calculation unit can improve the accuracy of its calculations by referring to relevant literature during the calculation process. For example, the calculation unit calculates prices by referring to relevant academic papers. For example, the calculation unit calculates prices by referring to relevant industry reports. For example, the calculation unit calculates prices by referring to relevant market research data. This enables the calculation unit to calculate prices with high accuracy by referring to relevant literature. Some or all of the above processes in the calculation unit may be performed using AI, for example, or without AI. For example, the calculation unit can use AI to evaluate relevant literature and improve the accuracy of its calculations.

[0086] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0087] The analysis unit can estimate the user's emotions and determine the priority of the analysis based on the estimated emotions. For example, if the user is stressed, it will prioritize the analysis of high-priority information and provide results quickly. If the user is relaxed, it will perform a detailed analysis and provide comprehensive results. If the user is in a hurry, it will perform a quick analysis based on the minimum necessary information. In this way, the analysis unit can provide the user with the optimal analysis results by determining the priority of the analysis based on the user's emotions. Emotion estimation is achieved using an emotion engine or generative AI, etc. Generative AI includes, but is not limited to, text generation AI and multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI or not using AI.

[0088] The data collection unit can estimate the user's emotions and adjust its information collection methods based on those emotions. For example, if the user is stressed, it can reduce the frequency of information collection and collect information when the user is relaxed. If the user is concentrating, it can adjust the timing of information collection to avoid interrupting their work. If the user is in a hurry, it can collect information quickly and provide the necessary information immediately. In this way, the data collection unit can reduce the user's burden by adjusting its information collection methods based on the user's emotions. Emotion estimation is achieved using an emotion engine or generative AI, etc. Generative AI includes, but is not limited to, text generation AI or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI or not using AI.

[0089] The calculation unit can estimate the user's emotions and adjust the display method of the calculated price based on the estimated user emotions. For example, if the user is nervous, it provides a simple and highly visible display method. If the user is relaxed, it provides a display method that includes detailed information. If the user is in a hurry, it provides a display method that gets straight to the point. In this way, the calculation unit can make the display easy for the user to understand by adjusting the price display method based on the user's emotions. Emotion estimation is achieved using an emotion engine or generative AI, etc. Generative AI includes, but is not limited to, text generation AI or multimodal generation AI. Some or all of the above processing in the calculation unit may be performed using AI or not using AI.

[0090] The analysis unit can estimate the user's emotions and adjust the presentation of the analysis based on the estimated emotions. For example, if the user is nervous, it provides a simple and easy-to-understand analysis result. If the user is relaxed, it provides a detailed analysis result. If the user is in a hurry, it provides a concise analysis result. In this way, the analysis unit can provide analysis results that are easy for the user to understand by adjusting the presentation of the analysis based on the user's emotions. Emotion estimation is achieved using an emotion engine or generative AI, etc. Generative AI includes, but is not limited to, text generation AI or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI or not using AI.

[0091] The calculation unit can estimate the user's emotions and determine the priority of prices to be calculated based on the estimated user emotions. For example, if the user is stressed, it can quickly calculate and provide a price. If the user is relaxed, it can perform a detailed price calculation and provide a price. If the user is in a hurry, it can calculate and provide a price based on the minimum necessary information. In this way, the calculation unit can provide a quick and appropriate price by determining the price priority based on the user's emotions. Emotion estimation is achieved using an emotion engine or generative AI, etc. Generative AI includes, but is not limited to, text generation AI or multimodal generation AI. Some or all of the above processing in the calculation unit may be performed using AI or not using AI.

[0092] The data collection unit can prioritize the collection of highly relevant information by considering the user's geographical location. For example, it can prioritize the collection of information related to the area the user is currently in. It can also collect and provide information about areas the user has visited in the past. Furthermore, it can collect and provide information about areas the user plans to visit in the future. In this way, the data collection unit can provide highly relevant information by considering geographical location. 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 analyze the user's geographical location using AI and prioritize the collection of highly relevant information.

[0093] The analysis unit can apply different analysis algorithms depending on the category of information. For example, a specialized analysis algorithm is applied to information about technical skills. An evaluation algorithm is applied to information about client satisfaction. A trend analysis algorithm is applied to information about market demand. In this way, the analysis unit can provide highly accurate analysis results by applying the appropriate analysis algorithm according to the category of information. Some or all of the above processing in the analysis unit may be performed using AI or not. For example, the analysis unit can apply different analysis algorithms using AI depending on the category of information.

[0094] The calculation unit can improve the accuracy of its calculations by considering the interrelationships of information. For example, it can calculate prices by considering the interrelationship between specialized knowledge and technical skills. It can calculate prices by considering the interrelationship between project performance and client satisfaction. It can calculate prices by considering the interrelationship between market demand and the competitive environment. In this way, the calculation unit can calculate prices with high accuracy by considering the interrelationships of information. Some or all of the above processing in the calculation unit may be performed using AI or not. For example, the calculation unit can use AI to evaluate the interrelationships of information and improve the accuracy of its calculations.

[0095] The data collection unit can analyze a user's social media activity and collect relevant information. For example, it can collect relevant information based on information shared by the user on social media. It can also analyze the content of posts from accounts the user follows and collect relevant information. It can analyze the activities of groups and communities the user participates in and collect relevant information. In this way, the data collection unit can provide relevant information by analyzing social media activity. 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 use AI to analyze a user's social media activity and collect relevant information.

[0096] The analysis unit can determine the priority of analysis based on when the information was collected. For example, it can prioritize the analysis of the latest information and lower the priority of analysis of older information. It can also adjust the level of detail of the analysis according to when the information was collected. In this way, the analysis unit can prioritize the analysis of the latest information by determining the priority of analysis based on when the information was collected. Some or all of the above processing in the analysis unit may be performed using AI or not. For example, the analysis unit can use AI to evaluate when the information was collected and determine the priority of analysis.

[0097] The following briefly describes the processing flow for example form 2.

[0098] Step 1: The data collection department gathers information about the skills and services offered by freelancers and sole proprietors. For example, the data collection department collects internal factors such as the freelancer's expertise and technical skills, years of experience, project track record, and client satisfaction. The data collection department can also collect external factors such as industry growth potential, market demand, competitive environment, local economic conditions, and the impact of laws and regulations. For example, the data collection department collects information such as the number and scale of successful projects the freelancer has completed in the past, as well as client ratings and feedback. Step 2: The analysis unit analyzes the information collected by the collection unit. For example, the analysis unit evaluates the value of the skills and services provided by freelancers and sole proprietors based on the collected internal and external factors. For instance, the analysis unit evaluates the freelancer's expertise and technical skills, years of experience, project track record, and client satisfaction, and provides a basis for calculating a fair price by considering industry growth potential, market demand, competitive environment, local economic conditions, and the impact of laws and regulations. Step 3: The calculation unit calculates a fair price based on the information analyzed by the analysis unit. For example, the calculation unit calculates a fair price based on the evaluated information, enabling freelancers and sole proprietors to provide services at a fair price.

[0099] 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.

[0100] 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.

[0101] 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.

[0102] Each of the multiple elements described above, including the data collection unit, analysis unit, and calculation unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the data collection unit uses the camera 42 and microphone 38B of the smart device 14 to collect information on freelancers and sole proprietors, and the control unit 46A transmits this information to the data processing unit 12. The analysis unit is implemented in the specific processing unit 290 of the data processing unit 12, for example, and analyzes the collected information to provide a basis for calculating a fair price. The calculation unit is implemented in the specific processing unit 290 of the data processing unit 12, for example, and calculates a fair price based on the analyzed information. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0103] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0104] 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.

[0105] 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.

[0106] 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.

[0107] 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.

[0108] 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).

[0109] 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.

[0110] 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.

[0111] 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.

[0112] 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.

[0113] 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.

[0114] 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.).

[0115] 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.

[0116] 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.

[0117] 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.

[0118] Each of the multiple elements described above, including the data collection unit, analysis unit, and calculation unit, is implemented, for example, in at least one of the smart glasses 214 and the data processing unit 12. For example, the data collection unit uses the camera 42 and microphone 238 of the smart glasses 214 to collect information on freelancers and sole proprietors, and the control unit 46A transmits this information to the data processing unit 12. The analysis unit is implemented, for example, in the specific processing unit 290 of the data processing unit 12, and analyzes the collected information to provide a basis for calculating a fair price. The calculation unit is implemented, for example, in the specific processing unit 290 of the data processing unit 12, and calculates a fair price based on the analyzed information. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0119] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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).

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.).

[0131] 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.

[0132] 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.

[0133] 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.

[0134] Each of the multiple elements described above, including the data collection unit, analysis unit, and calculation unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the data collection unit uses the camera 42 and microphone 238 of the headset terminal 314 to collect information on freelancers and sole proprietors, and the control unit 46A transmits this information to the data processing unit 12. The analysis unit is implemented in the specific processing unit 290 of the data processing unit 12, for example, and analyzes the collected information to provide a basis for calculating a fair price. The calculation unit is implemented in the specific processing unit 290 of the data processing unit 12, for example, and calculates a fair price based on the analyzed information. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0135] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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).

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.).

[0148] 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.

[0149] 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.

[0150] 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.

[0151] Each of the multiple elements described above, including the data collection unit, analysis unit, and calculation unit, is implemented, for example, in at least one of the robot 414 and the data processing unit 12. For example, the data collection unit uses the camera 42 and microphone 238 of the robot 414 to collect information on freelancers and sole proprietors, and the control unit 46A transmits this information to the data processing unit 12. The analysis unit is implemented, for example, in the specific processing unit 290 of the data processing unit 12, and provides a basis for analyzing the collected information to calculate a fair price. The calculation unit is implemented, for example, in the specific processing unit 290 of the data processing unit 12, and calculates a fair price based on the analyzed information. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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."

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] (Note 1) A collection department that collects information on skills and services provided by freelancers and sole proprietors, An analysis unit analyzes the information collected by the aforementioned collection unit, The system includes a calculation unit that calculates an appropriate price based on the information analyzed by the analysis unit. A system characterized by the following features. (Note 2) The aforementioned collection unit is Collect internal information on freelancers and sole proprietors, including their expertise, technical skills, years of experience, project track record, and client satisfaction. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned collection unit is Gather external factors such as industry growth potential, market demand, competitive landscape, regional economic conditions, and the impact of laws and regulations. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned analysis unit, Based on the collected internal and external factors, we evaluate the value of the skills and services provided by freelancers and sole proprietors. The system described in Appendix 1, characterized by the features described herein. (Note 5) The calculation unit described above, A fair price is calculated based on the evaluated information. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned analysis unit, We help freelancers set appropriate prices, taking into account the competitive environment and the impact of legal regulations. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned collection unit is It estimates the user's emotions and adjusts the timing of information collection based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned collection unit is Analyze the past project history of freelancers and sole proprietors to select the most effective information gathering method. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned collection unit is When gathering information, filtering is performed based on the user's current projects and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned collection unit is It estimates the user's emotions and prioritizes the information to collect based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned collection unit is When collecting information, the system prioritizes collecting highly relevant information by considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned collection unit is When gathering information, we analyze users' social media activity and collect relevant information. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned analysis unit, The system estimates the user's emotions and adjusts the representation of the analysis based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit, During analysis, the level of detail of the analysis is adjusted based on the importance of the collected information. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit, During analysis, different analysis algorithms are applied depending on the category of information. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit, It estimates the user's emotions and adjusts the length of the analysis based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit, During analysis, the priority of the analysis is determined based on when the information was collected. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned analysis unit, During analysis, the order of analysis is adjusted based on the relevance of the information. The system described in Appendix 1, characterized by the features described herein. (Note 19) The calculation unit described above, It estimates user sentiment and determines price priorities based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 20) The calculation unit described above, When calculating, the interrelationships between the information are taken into consideration to improve the accuracy of the calculation. The system described in Appendix 1, characterized by the features described herein. (Note 21) The calculation unit described above, The calculation takes into account the attribute information of the information submitter. The system described in Appendix 1, characterized by the features described herein. (Note 22) The calculation unit described above, We estimate user sentiment and adjust how prices are displayed based on that estimated sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 23) The calculation unit described above, The calculation takes into account the geographical distribution of the information. The system described in Appendix 1, characterized by the features described herein. (Note 24) The calculation unit described above, During calculation, we refer to relevant literature to improve the accuracy of the calculation. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

[0171] 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 collects information on skills and services provided by freelancers and sole proprietors, An analysis unit analyzes the information collected by the aforementioned collection unit, The system includes a calculation unit that calculates an appropriate price based on the information analyzed by the analysis unit. A system characterized by the following features.

2. The aforementioned collection unit is Collect internal information on freelancers and sole proprietors, including their expertise, technical skills, years of experience, project track record, and client satisfaction. The system according to feature 1.

3. The aforementioned collection unit is Gather external factors such as industry growth potential, market demand, competitive landscape, regional economic conditions, and the impact of laws and regulations. The system according to feature 1.

4. The aforementioned analysis unit, Based on the collected internal and external factors, we evaluate the value of the skills and services provided by freelancers and sole proprietors. The system according to feature 1.

5. The calculation unit described above, A fair price is calculated based on the evaluated information. The system according to feature 1.

6. The aforementioned analysis unit, We help freelancers set appropriate prices, taking into account the competitive environment and the impact of legal regulations. The system according to feature 1.

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

8. The aforementioned collection unit is Analyze the past project history of freelancers and sole proprietors to select the most effective information gathering method. The system according to feature 1.

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