system

Abstract

The system according to this embodiment aims to provide efficient support tailored to the work style and needs of freelancers. [Solution] The system according to the embodiment comprises a collection unit, a generation unit, and a provision unit. The collection unit grasps the work style and needs of freelancers. The generation unit efficiently responds based on the information collected by the collection unit, utilizing online tools and generation AI. The provision unit provides high-quality support based on the information generated by the generation unit.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the prior art, there was a problem that it was difficult to provide efficient support according to the work styles and needs of freelancers.

[0005] The system according to the embodiment aims to provide efficient support according to the work styles and needs of freelancers.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a collection unit, a generation unit, and a provision unit. The collection unit understands the work style and needs of freelancers. The generation unit efficiently responds to the information collected by the collection unit by utilizing online tools and generation AI. The provision unit provides high-quality support based on the information generated by the generation unit. [Effects of the Invention]

[0007] The system according to this embodiment can provide efficient support tailored to the work style and needs of freelancers. [Brief explanation of the drawing]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The freelance assistant system according to an embodiment of the present invention is a system that provides support services, primarily by handling back-office tasks, so that freelancers can concentrate on front-office work. The freelance assistant system handles detailed tasks other than those that freelancers are responsible for, such as scheduling, replying to emails, creating documents, translation, and data entry. The demand for this service is expanding as the number of freelancers increases. Freelancers often have to handle all tasks themselves, which can prevent them from focusing on their business. Therefore, there is a growing demand for support from freelance assistants so that they can concentrate on their areas of expertise. For example, when providing a freelance assistant service, it is necessary to first make freelancers aware of the service. For example, marketing using social media, blogs, and portal sites is important. In addition, when providing the service, it is necessary to customize it to suit the work style and needs of freelancers. For example, efficient and speedy responses are required by utilizing online tools, chatbots, and generative AI. Ultimately, it is important to expand the market and acquire customers by providing high-quality support so that freelancers can concentrate on their business. This allows the freelance assistant system to provide high-quality support, enabling freelancers to focus on their own businesses.

[0029] The freelance assistant system according to this embodiment comprises a collection unit, a generation unit, and a provision unit. The collection unit understands the freelancer's work style and needs. The collection unit understands the freelancer's work style and needs by, for example, conducting surveys or interviews. The collection unit can collect information on the freelancer's work style and needs through, for example, online surveys. The collection unit can also collect detailed information through face-to-face interviews. Furthermore, the collection unit can analyze the freelancer's work history and select the optimal collection method. For example, the collection unit analyzes the types and frequency of projects the freelancer has undertaken in the past and includes relevant questions in the survey. The generation unit efficiently responds based on the information collected by the collection unit, utilizing online tools and generation AI. For example, the generation unit uses generation AI to analyze the freelancer's calendar and propose an optimal schedule. The generation unit can also use generation AI to generate email replies and send them after confirmation by the freelancer. For example, the generation unit uses a generation AI to generate email reply content, which is then sent after confirmation by the freelancer. The provision unit provides high-quality support based on the information generated by the generation unit. The provision unit can, for example, assign staff with specialized knowledge to provide support tailored to specific tasks. The provision unit can also, for example, provide support tailored to the tools and systems used by the freelancer. As a result, the freelance assistant system according to this embodiment can understand the freelancer's work style and needs and provide efficient and high-quality support.

[0030] The data collection department employs various methods to gather information in order to understand the work styles and needs of freelancers. Specifically, it conducts online surveys and face-to-face interviews to collect detailed information on freelancers' work content, work environment, tools used, working hours, project types, frequency, and compensation structure. Online surveys are designed to be easily answered using devices and platforms that freelancers use on a daily basis, and the response data is automatically stored in a database. Face-to-face interviews allow interviewers to directly interact with freelancers to gain deeper insights and elicit specific examples and experiences. This enables the data collection department to gain a detailed understanding of the diverse work styles and individual needs of freelancers. Furthermore, the data collection department analyzes the work history of freelancers to extract past project types and frequencies, success stories, and challenges. This allows the data collection department to understand the work patterns and trends of freelancers and select the most optimal collection method. For example, it creates surveys tailored to specific industries or work content to collect more accurate data. In addition, the data collection department statistically analyzes the collected data to reveal overall trends and common needs of freelancers. This allows the data collection unit to comprehensively understand the work styles and needs of freelancers and reflect them in the overall system design and operation.

[0031] The generation unit efficiently responds to information collected by the collection unit by utilizing online tools and generation AI. Specifically, it uses generation AI to analyze freelancers' calendars and propose optimal schedules. The generation AI analyzes the freelancer's past schedule data and current plans to propose ways to optimize work priorities and time allocation. For example, the generation AI considers the deadlines and importance of past projects the freelancer has completed and automatically adjusts future schedules. The generation unit can also use generation AI to generate email replies, which are then sent after review by the freelancer. The generation AI analyzes the content of emails received by freelancers and generates appropriate replies. For example, in response to client inquiries, it generates quick and accurate replies based on past correspondence and project progress. The generated replies are reviewed by the freelancer, modified as needed, and then sent. This allows the generation unit to significantly improve the efficiency of freelancers' work. Furthermore, the generation unit can also provide customized tools and templates tailored to the freelancer's specific work. For example, it can generate project management tools tailored to specific tasks or frequently used email templates to support freelancers in efficiently carrying out their work. This allows the generation unit to maximize the freelancer's work efficiency and deliver higher quality results.

[0032] The service provider department provides high-quality support based on the information generated by the service provider department. Specifically, it assigns staff with specialized knowledge to provide support tailored to specific tasks. For example, IT-related projects are supported by staff proficient in programming and system design, while design-related projects are handled by staff knowledgeable in graphic design and UI / UX design. This allows the service provider department to provide support that leverages specialized knowledge and experience to address the specific challenges faced by freelancers. The service provider department can also provide support that is compatible with the tools and systems used by freelancers. For example, it can provide support for specific project management tools and design software, offering advice and troubleshooting to help freelancers work efficiently. Furthermore, the service provider department can provide customized support tailored to the freelancer's work style and needs. For example, if a freelancer is working remotely, it can enhance online support and provide real-time support via video conferencing or chat as needed. The service provider department can also collect feedback from freelancers and use it to improve support and develop new services. This allows the service provider department to not only improve the efficiency of freelancers' work but also build long-term trusting relationships. Furthermore, the service provider can offer training and workshops to support the growth of freelancers, helping them improve their skills and advance their careers. This allows the service provider to help freelancers perform at a higher level and enhance the overall value of the system.

[0033] The data collection unit can understand the work style and needs of freelancers by conducting surveys and interviews. For example, the data collection unit can collect information on freelancers' work styles and needs through online surveys. It can also collect detailed information through face-to-face interviews. For example, the data collection unit can set up questions regarding freelancers' work styles and needs and conduct an online survey. Furthermore, the data collection unit can conduct face-to-face interviews to collect detailed information on freelancers' work styles and needs. In addition, the data collection unit can analyze freelancers' work history and select the optimal data collection method. For example, the data collection unit can analyze the types and frequency of projects freelancers have undertaken in the past and include relevant questions in the survey. This allows the data collection unit to accurately understand freelancers' work styles and needs through surveys and interviews. Some or all of the above processes in the data collection unit may be performed using AI, or not. For example, the data collection unit can input survey response data into a generating AI and have the generating AI analyze the response data.

[0034] The generation unit can analyze a freelancer's calendar using a generation AI and propose an optimal schedule. For example, the generation unit can use a generation AI to analyze a freelancer's calendar and propose an optimal schedule. The generation unit can also use a generation AI to analyze a freelancer's calendar and propose an optimal schedule. For example, the generation unit can use a generation AI to analyze a freelancer's calendar and propose an optimal schedule. In this way, the generation unit can use a generation AI to efficiently analyze a freelancer's calendar and propose an optimal schedule. Some or all of the above processing in the generation unit is performed using a generation AI. For example, the generation unit can input freelancer calendar data into a generation AI and have the generation AI propose an optimal schedule.

[0035] The generation unit can generate email reply content using a generation AI, and send it after confirmation by a freelancer. For example, the generation unit can have the generation AI generate the email reply content, and send it after confirmation by a freelancer. The generation unit can also generate email reply content using a generation AI, and send it after confirmation by a freelancer. For example, the generation unit can have the generation AI generate the email reply content, and send it after confirmation by a freelancer. This allows the generation unit to efficiently generate email reply content using a generation AI and send it after confirmation by a freelancer. Some or all of the above-described processes in the generation unit are performed using a generation AI. For example, the generation unit can input prompts for generating email reply content into the generation AI, and have the generation AI execute the generation of the reply content.

[0036] The service provider can assign staff with specialized knowledge to provide support tailored to specific tasks. For example, the service provider can assign staff with specialized knowledge to provide support tailored to specific tasks. The service provider can assign staff with specialized knowledge to provide support tailored to specific tasks. The service provider can also assign staff with specialized knowledge to provide support tailored to specific tasks. For example, the service provider can assign staff with specialized knowledge to provide support tailored to specific tasks. This allows the service provider to provide high-quality support tailored to specific tasks by assigning staff with specialized knowledge. Some or all of the above processes in the service provider may be performed using AI or not. For example, the service provider can input the assignment of staff to provide support tailored to specific tasks into a generating AI and have the generating AI perform the staff assignment.

[0037] The service provider can provide support tailored to the tools and systems used by freelancers. For example, the service provider can provide support tailored to the tools and systems used by freelancers. The service provider can also provide support tailored to the tools and systems used by freelancers. For example, the service provider can provide support tailored to the tools and systems used by freelancers. This allows the service provider to ensure smooth business operations by adapting to the tools and systems used by freelancers. Some or all of the above-described processes in the service provider may be performed using AI or not. For example, the service provider can input information for providing support tailored to the tools and systems used by freelancers into a generating AI, and have the generating AI perform the support provision.

[0038] The data collection unit can analyze a freelancer's past work history and select the optimal data collection method. For example, the unit can analyze the types and frequency of projects a freelancer has worked on in the past and include relevant questions in the questionnaire. For example, the unit can plan interviews to collect detailed information about specific tasks from the freelancer's past work history. The unit can also prioritize collecting questions about specific skills and experiences based on the freelancer's past work history. This allows the unit to select the optimal data collection method and efficiently collect information by analyzing past work history. Some or all of the above processes in the data collection unit may or may not be performed using AI. For example, the data collection unit can input the freelancer's past work history data into a generating AI and have the generating AI select the optimal data collection method.

[0039] The data collection unit can filter data based on the freelancer's current projects and areas of interest during the collection process. For example, the unit can prioritize collecting questions related to the freelancer's current projects. The unit can also filter relevant information and adjust the content of questionnaires and interviews based on the freelancer's areas of interest. Furthermore, the unit can collect information at appropriate times depending on the progress of the freelancer's current projects. This allows the unit to efficiently collect highly relevant information by filtering it based on current projects and areas of interest. Some or all of the above processing in the data collection unit may or may not be performed using AI. For example, the data collection unit can input data on the freelancer's current projects and areas of interest into a generating AI and have the generating AI perform the information filtering.

[0040] The data collection unit can prioritize collecting highly relevant information by considering the freelancer's geographical location during the collection process. For example, if a freelancer is working in a specific region, the data collection unit will prioritize collecting information related to that region. For example, the data collection unit will collect information on region-specific needs and challenges based on the freelancer's geographical location. Furthermore, if a freelancer is on the move, the data collection unit can collect information related to their current location in real time. This allows the data collection unit to efficiently collect information on region-specific needs and challenges 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 input the freelancer's geographical location into a generating AI and have the generating AI collect highly relevant information.

[0041] The data collection unit can analyze the freelancer's social media activity and collect relevant information during the collection process. For example, the data collection unit can analyze the freelancer's social media posts and collect information related to topics of interest. For example, the data collection unit can analyze the freelancer's followers and network and collect information related to relevant industries and fields. The data collection unit can also collect information on current trends and topics from the freelancer's social media activity. This allows the data collection unit to efficiently collect information on topics of interest and current trends 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 input the freelancer's social media activity data into a generating AI and have the generating AI perform the collection of relevant information.

[0042] The generation unit can adjust the level of detail in the generated content based on the importance of the freelancer's work during the generation process. For example, the generation unit will create a detailed schedule for high-priority tasks, and a simplified schedule for low-priority tasks. The generation unit can also create a schedule that takes into account the necessary resources and time, depending on the importance of the task. This allows the generation unit to efficiently perform tasks by adjusting the level of detail in the generated content according to the importance of the task. Some or all of the above processing in the generation unit is performed using the generation AI. For example, the generation unit can input data on the importance of the freelancer's work into the generation AI and have the generation AI adjust the level of detail in the generated content.

[0043] The generation unit can apply different generation algorithms depending on the freelancer's work category during generation. For example, the generation unit applies an efficient schedule generation algorithm to scheduling tasks. For example, it applies a reply content generation algorithm using natural language processing to email reply tasks. The generation unit can also apply a template-based generation algorithm to document creation tasks. In this way, the generation unit can efficiently perform tasks by applying the appropriate generation algorithm according to the work category. Some or all of the above processing in the generation unit is performed using a generation AI. For example, the generation unit can input freelancer work category data into the generation AI and have the generation AI execute the application of the generation algorithm.

[0044] The generation unit can determine the priority of generated content based on the submission deadlines of freelancers' work during the generation process. For example, the generation unit will prioritize generating content for tasks with approaching deadlines. For example, it will postpone generating content for tasks with distant submission deadlines. The generation unit can also adjust the level of detail and priority of the generated content according to the submission deadline. This allows the generation unit to efficiently perform its tasks by determining the priority of generated content according to the submission deadline. Some or all of the above processing in the generation unit is performed using a generation AI. For example, the generation unit can input data on the submission deadlines of freelancers' work into the generation AI and have the generation AI determine the priority of the generated content.

[0045] The generation unit can adjust the order of generated content based on the relevance of the freelancer's tasks during generation. For example, the generation unit can prioritize generating content for highly relevant tasks. For example, it can postpone generating content for less relevant tasks. The generation unit can also adjust the order and level of detail of the generated content according to the relevance of the tasks. This allows the generation unit to efficiently perform tasks by adjusting the order of generated content according to the relevance of the tasks. Some or all of the above processing in the generation unit is performed using a generation AI. For example, the generation unit can input data on the relevance of the freelancer's tasks into the generation AI and have the generation AI perform the adjustment of the order of generated content.

[0046] The service provider can analyze the freelancer's past support history to select the optimal support method at the time of service provision. For example, the service provider can analyze the support the freelancer has received in the past and provide similar support. For example, the service provider can select an effective support method from the freelancer's past support history. The service provider can also propose the optimal support method based on the freelancer's past support history. In this way, the service provider can select the optimal support method by analyzing past support history and provide support efficiently. Some or all of the above processes in the service provider may be performed using AI or not. For example, the service provider can input the freelancer's past support history data into a generating AI and have the generating AI select the optimal support method.

[0047] The service provider can customize the means of support based on the freelancer's current work situation at the time of provision. For example, the service provider can provide support related to the project the freelancer is currently working on. For example, the service provider can customize the necessary means of support according to the freelancer's current work situation. The service provider can also propose the most suitable support method considering the freelancer's current work situation. In this way, the service provider can provide more appropriate support by customizing the means of support based on the current work situation. Some or all of the above processing in the service provider may be performed using AI or not. For example, the service provider can input data on the freelancer's current work situation into a generating AI and have the generating AI perform the customization of the means of support.

[0048] The service provider can select the optimal support method by considering the freelancer's geographical location information at the time of service provision. For example, if the freelancer is working in a specific region, the service provider will provide support relevant to that region. For example, based on the freelancer's geographical location information, the service provider will provide support that addresses region-specific needs and challenges. Furthermore, if the freelancer is on the move, the service provider can provide support relevant to their current location in real time. In this way, the service provider can provide support that addresses region-specific needs and challenges by considering geographical location information. Some or all of the above processing in the service provider may be performed using AI or not. For example, the service provider can input the freelancer's geographical location information into a generating AI and have the generating AI select the optimal support method.

[0049] The service provider can analyze a freelancer's social media activity and propose support measures at the time of service provision. For example, the service provider can analyze a freelancer's social media posts and provide support related to topics of interest. For example, the service provider can analyze a freelancer's followers and network and provide support related to relevant industries and fields. The service provider can also provide support regarding current trends and topics based on the freelancer's social media activity. In this way, the service provider can provide support regarding topics of interest and current trends by analyzing social media activity. Some or all of the above processing in the service provider may be performed using AI or not. For example, the service provider can input freelancer social media activity data into a generating AI and have the generating AI propose support measures.

[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 department can analyze freelancers' social media activity to understand their work style and needs. For example, it can analyze freelancers' social media posts to identify topics of interest and current trends. It can also analyze freelancers' followers and networks to gather information on relevant industries and fields. Furthermore, it can gather information on current projects and areas of interest from freelancers' social media activity. This allows the data collection department to gain a more accurate understanding of freelancers' work style and needs through their social media activity.

[0052] The data collection unit can prioritize collecting highly relevant information by considering the geographical location of freelancers. For example, if a freelancer is working in a specific region, the unit will prioritize collecting information related to that region. Based on the freelancer's geographical location, the unit collects information on region-specific needs and challenges. Furthermore, if a freelancer is on the move, the unit can collect information related to their current location in real time. This allows the unit to efficiently collect information on region-specific needs and challenges by considering geographical location.

[0053] The generation unit can adjust the level of detail in the generated content based on the importance of the freelancer's work. For example, the generation unit generates a detailed schedule for high-priority tasks and a simplified schedule for low-priority tasks. Furthermore, the generation unit can generate schedules that take into account the necessary resources and time, depending on the importance of the task. This allows the generation unit to efficiently execute tasks by adjusting the level of detail in the generated content according to the importance of the task.

[0054] The service provider can analyze the freelancer's past support history to select the most suitable support method at the time of provision. For example, the service provider can analyze the support the freelancer has received in the past and provide similar support. The service provider selects an effective support method based on the freelancer's past support history. Furthermore, the service provider can propose the most suitable support method based on the freelancer's past support history. In this way, the service provider can select the most suitable support method by analyzing past support history and provide support efficiently.

[0055] The generation unit can apply different generation algorithms depending on the freelancer's work category during the generation process. For example, the generation unit applies an efficient schedule generation algorithm to scheduling tasks. For email reply tasks, it applies a reply content generation algorithm using natural language processing. Furthermore, the generation unit can apply a template-based generation algorithm to document creation tasks. This allows the generation unit to efficiently perform tasks by applying the appropriate generation algorithm according to the work category.

[0056] The service provider can customize the support provided based on the freelancer's current work situation. For example, the service provider can provide support related to the project the freelancer is currently working on. The service provider customizes the necessary support methods according to the freelancer's current work situation. The service provider can also propose the most suitable support method considering the freelancer's current work situation. In this way, the service provider can provide more appropriate support by customizing the support methods based on the current work situation.

[0057] The following briefly describes the processing flow for example form 1.

[0058] Step 1: The data collection team understands the freelancers' work styles and needs. For example, the team conducts surveys and interviews, gathering detailed information through online questionnaires and in-person interviews. The team can also analyze the freelancers' work history to select the most suitable data collection method. For example, they can analyze the types and frequency of past projects and include relevant questions in the survey. Step 2: The generation unit efficiently responds to the information collected by the collection unit by utilizing online tools and generation AI. For example, it can use generation AI to analyze a freelancer's calendar and propose an optimal schedule. It can also use generation AI to generate email replies, which can then be sent after confirmation by the freelancer. Step 3: The providing department provides high-quality support based on the information generated by the generating department. For example, they may assign staff with specialized knowledge to provide support tailored to specific tasks. They can also provide support that is compatible with the tools and systems used by freelancers.

[0059] (Example of form 2) The freelance assistant system according to an embodiment of the present invention is a system that provides support services, primarily by handling back-office tasks, so that freelancers can concentrate on front-office work. The freelance assistant system handles detailed tasks other than those that freelancers are responsible for, such as scheduling, replying to emails, creating documents, translation, and data entry. The demand for this service is expanding as the number of freelancers increases. Freelancers often have to handle all tasks themselves, which can prevent them from focusing on their business. Therefore, there is a growing demand for support from freelance assistants so that they can concentrate on their areas of expertise. For example, when providing a freelance assistant service, it is necessary to first make freelancers aware of the service. For example, marketing using social media, blogs, and portal sites is important. In addition, when providing the service, it is necessary to customize it to suit the work style and needs of freelancers. For example, efficient and speedy responses are required by utilizing online tools, chatbots, and generative AI. Ultimately, it is important to expand the market and acquire customers by providing high-quality support so that freelancers can concentrate on their business. This allows the freelance assistant system to provide high-quality support, enabling freelancers to focus on their own businesses.

[0060] The freelance assistant system according to this embodiment comprises a collection unit, a generation unit, and a provision unit. The collection unit understands the freelancer's work style and needs. The collection unit understands the freelancer's work style and needs by, for example, conducting surveys or interviews. The collection unit can collect information on the freelancer's work style and needs through, for example, online surveys. The collection unit can also collect detailed information through face-to-face interviews. Furthermore, the collection unit can analyze the freelancer's work history and select the optimal collection method. For example, the collection unit analyzes the types and frequency of projects the freelancer has undertaken in the past and includes relevant questions in the survey. The generation unit efficiently responds based on the information collected by the collection unit, utilizing online tools and generation AI. For example, the generation unit uses generation AI to analyze the freelancer's calendar and propose an optimal schedule. The generation unit can also use generation AI to generate email replies and send them after confirmation by the freelancer. For example, the generation unit uses a generation AI to generate email reply content, which is then sent after confirmation by the freelancer. The provision unit provides high-quality support based on the information generated by the generation unit. The provision unit can, for example, assign staff with specialized knowledge to provide support tailored to specific tasks. The provision unit can also, for example, provide support tailored to the tools and systems used by the freelancer. As a result, the freelance assistant system according to this embodiment can understand the freelancer's work style and needs and provide efficient and high-quality support.

[0061] The data collection department employs various methods to gather information in order to understand the work styles and needs of freelancers. Specifically, it conducts online surveys and face-to-face interviews to collect detailed information on freelancers' work content, work environment, tools used, working hours, project types, frequency, and compensation structure. Online surveys are designed to be easily answered using devices and platforms that freelancers use on a daily basis, and the response data is automatically stored in a database. Face-to-face interviews allow interviewers to directly interact with freelancers to gain deeper insights and elicit specific examples and experiences. This enables the data collection department to gain a detailed understanding of the diverse work styles and individual needs of freelancers. Furthermore, the data collection department analyzes the work history of freelancers to extract past project types and frequencies, success stories, and challenges. This allows the data collection department to understand the work patterns and trends of freelancers and select the most optimal collection method. For example, it creates surveys tailored to specific industries or work content to collect more accurate data. In addition, the data collection department statistically analyzes the collected data to reveal overall trends and common needs of freelancers. This allows the data collection unit to comprehensively understand the work styles and needs of freelancers and reflect them in the overall system design and operation.

[0062] The generation unit efficiently responds to information collected by the collection unit by utilizing online tools and generation AI. Specifically, it uses generation AI to analyze freelancers' calendars and propose optimal schedules. The generation AI analyzes the freelancer's past schedule data and current plans to propose ways to optimize work priorities and time allocation. For example, the generation AI considers the deadlines and importance of past projects the freelancer has completed and automatically adjusts future schedules. The generation unit can also use generation AI to generate email replies, which are then sent after review by the freelancer. The generation AI analyzes the content of emails received by freelancers and generates appropriate replies. For example, in response to client inquiries, it generates quick and accurate replies based on past correspondence and project progress. The generated replies are reviewed by the freelancer, modified as needed, and then sent. This allows the generation unit to significantly improve the efficiency of freelancers' work. Furthermore, the generation unit can also provide customized tools and templates tailored to the freelancer's specific work. For example, it can generate project management tools tailored to specific tasks or frequently used email templates to support freelancers in efficiently carrying out their work. This allows the generation unit to maximize the freelancer's work efficiency and deliver higher quality results.

[0063] The service provider department provides high-quality support based on the information generated by the service provider department. Specifically, it assigns staff with specialized knowledge to provide support tailored to specific tasks. For example, IT-related projects are supported by staff proficient in programming and system design, while design-related projects are handled by staff knowledgeable in graphic design and UI / UX design. This allows the service provider department to provide support that leverages specialized knowledge and experience to address the specific challenges faced by freelancers. The service provider department can also provide support that is compatible with the tools and systems used by freelancers. For example, it can provide support for specific project management tools and design software, offering advice and troubleshooting to help freelancers work efficiently. Furthermore, the service provider department can provide customized support tailored to the freelancer's work style and needs. For example, if a freelancer is working remotely, it can enhance online support and provide real-time support via video conferencing or chat as needed. The service provider department can also collect feedback from freelancers and use it to improve support and develop new services. This allows the service provider department to not only improve the efficiency of freelancers' work but also build long-term trusting relationships. Furthermore, the service provider can offer training and workshops to support the growth of freelancers, helping them improve their skills and advance their careers. This allows the service provider to help freelancers perform at a higher level and enhance the overall value of the system.

[0064] The data collection unit can understand the work style and needs of freelancers by conducting surveys and interviews. For example, the data collection unit can collect information on freelancers' work styles and needs through online surveys. It can also collect detailed information through face-to-face interviews. For example, the data collection unit can set up questions regarding freelancers' work styles and needs and conduct an online survey. Furthermore, the data collection unit can conduct face-to-face interviews to collect detailed information on freelancers' work styles and needs. In addition, the data collection unit can analyze freelancers' work history and select the optimal data collection method. For example, the data collection unit can analyze the types and frequency of projects freelancers have undertaken in the past and include relevant questions in the survey. This allows the data collection unit to accurately understand freelancers' work styles and needs through surveys and interviews. Some or all of the above processes in the data collection unit may be performed using AI, or not. For example, the data collection unit can input survey response data into a generating AI and have the generating AI analyze the response data.

[0065] The generation unit can analyze a freelancer's calendar using a generation AI and propose an optimal schedule. For example, the generation unit can use a generation AI to analyze a freelancer's calendar and propose an optimal schedule. The generation unit can also use a generation AI to analyze a freelancer's calendar and propose an optimal schedule. For example, the generation unit can use a generation AI to analyze a freelancer's calendar and propose an optimal schedule. In this way, the generation unit can use a generation AI to efficiently analyze a freelancer's calendar and propose an optimal schedule. Some or all of the above processing in the generation unit is performed using a generation AI. For example, the generation unit can input freelancer calendar data into a generation AI and have the generation AI propose an optimal schedule.

[0066] The generation unit can generate email reply content using a generation AI, and send it after confirmation by a freelancer. For example, the generation unit can have the generation AI generate the email reply content, and send it after confirmation by a freelancer. The generation unit can also generate email reply content using a generation AI, and send it after confirmation by a freelancer. For example, the generation unit can have the generation AI generate the email reply content, and send it after confirmation by a freelancer. This allows the generation unit to efficiently generate email reply content using a generation AI and send it after confirmation by a freelancer. Some or all of the above-described processes in the generation unit are performed using a generation AI. For example, the generation unit can input prompts for generating email reply content into the generation AI, and have the generation AI execute the generation of the reply content.

[0067] The service provider can assign staff with specialized knowledge to provide support tailored to specific tasks. For example, the service provider can assign staff with specialized knowledge to provide support tailored to specific tasks. The service provider can assign staff with specialized knowledge to provide support tailored to specific tasks. The service provider can also assign staff with specialized knowledge to provide support tailored to specific tasks. For example, the service provider can assign staff with specialized knowledge to provide support tailored to specific tasks. This allows the service provider to provide high-quality support tailored to specific tasks by assigning staff with specialized knowledge. Some or all of the above processes in the service provider may be performed using AI or not. For example, the service provider can input the assignment of staff to provide support tailored to specific tasks into a generating AI and have the generating AI perform the staff assignment.

[0068] The service provider can provide support tailored to the tools and systems used by freelancers. For example, the service provider can provide support tailored to the tools and systems used by freelancers. The service provider can also provide support tailored to the tools and systems used by freelancers. For example, the service provider can provide support tailored to the tools and systems used by freelancers. This allows the service provider to ensure smooth business operations by adapting to the tools and systems used by freelancers. Some or all of the above-described processes in the service provider may be performed using AI or not. For example, the service provider can input information for providing support tailored to the tools and systems used by freelancers into a generating AI, and have the generating AI perform the support provision.

[0069] The data collection unit can estimate the freelancer's emotions and adjust the content of questionnaires and interviews based on the estimated emotions. For example, if a freelancer is stressed, the data collection unit will prioritize creating questionnaires with concise and easy-to-answer questions. If a freelancer is relaxed, the data collection unit will increase the number of open-ended questions to elicit more detailed information. If a freelancer is busy, the data collection unit can also reduce the number of questions and increase the number of choices to allow for quicker responses. In this way, the data collection unit can collect more relevant information by adjusting the content of questions according to the freelancer's emotions. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input questionnaire response data into a generative AI and have the generative AI perform analysis of the response data.

[0070] The data collection unit can analyze a freelancer's past work history and select the optimal data collection method. For example, the unit can analyze the types and frequency of projects a freelancer has worked on in the past and include relevant questions in the questionnaire. For example, the unit can plan interviews to collect detailed information about specific tasks from the freelancer's past work history. The unit can also prioritize collecting questions about specific skills and experiences based on the freelancer's past work history. This allows the unit to select the optimal data collection method and efficiently collect information by analyzing past work history. Some or all of the above processes in the data collection unit may or may not be performed using AI. For example, the data collection unit can input the freelancer's past work history data into a generating AI and have the generating AI select the optimal data collection method.

[0071] The data collection unit can filter data based on the freelancer's current projects and areas of interest during the collection process. For example, the unit can prioritize collecting questions related to the freelancer's current projects. The unit can also filter relevant information and adjust the content of questionnaires and interviews based on the freelancer's areas of interest. Furthermore, the unit can collect information at appropriate times depending on the progress of the freelancer's current projects. This allows the unit to efficiently collect highly relevant information by filtering it based on current projects and areas of interest. Some or all of the above processing in the data collection unit may or may not be performed using AI. For example, the data collection unit can input data on the freelancer's current projects and areas of interest into a generating AI and have the generating AI perform the information filtering.

[0072] The data collection unit can estimate the freelancer's emotions and prioritize the information to collect based on those emotions. For example, if the freelancer is stressed, the data collection unit will prioritize collecting high-priority information. If the freelancer is relaxed, the data collection unit will take more time to collect detailed information. If the freelancer is busy, the data collection unit can also prioritize information that can be collected quickly. In this way, the data collection unit can prioritize the collection of important information by prioritizing information according to the freelancer's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input the freelancer's emotion data into a generative AI and have the generative AI perform the determination of information prioritization.

[0073] The data collection unit can prioritize collecting highly relevant information by considering the freelancer's geographical location during the collection process. For example, if a freelancer is working in a specific region, the data collection unit will prioritize collecting information related to that region. For example, the data collection unit will collect information on region-specific needs and challenges based on the freelancer's geographical location. Furthermore, if a freelancer is on the move, the data collection unit can collect information related to their current location in real time. This allows the data collection unit to efficiently collect information on region-specific needs and challenges 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 input the freelancer's geographical location into a generating AI and have the generating AI collect highly relevant information.

[0074] The data collection unit can analyze the freelancer's social media activity and collect relevant information during the collection process. For example, the data collection unit can analyze the freelancer's social media posts and collect information related to topics of interest. For example, the data collection unit can analyze the freelancer's followers and network and collect information related to relevant industries and fields. The data collection unit can also collect information on current trends and topics from the freelancer's social media activity. This allows the data collection unit to efficiently collect information on topics of interest and current trends 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 input the freelancer's social media activity data into a generating AI and have the generating AI perform the collection of relevant information.

[0075] The generation unit can estimate the freelancer's emotions and adjust the method of suggesting schedules based on the estimated emotions. For example, if the freelancer is stressed, the generation unit's AI may suggest a schedule with ample leeway. If the freelancer is relaxed, the generation unit's AI may suggest an efficient schedule. If the freelancer is busy, the generation unit's AI may also suggest a schedule that prioritizes important tasks. In this way, the generation unit can suggest a more appropriate schedule by adjusting the method of suggesting schedules according to the freelancer's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the generation unit is performed using the generation AI. For example, the generation unit can input the freelancer's emotion data into the generation AI and have the generation AI adjust the method of suggesting schedules.

[0076] The generation unit can adjust the level of detail in the generated content based on the importance of the freelancer's work during the generation process. For example, the generation unit will create a detailed schedule for high-priority tasks, and a simplified schedule for low-priority tasks. The generation unit can also create a schedule that takes into account the necessary resources and time, depending on the importance of the task. This allows the generation unit to efficiently perform tasks by adjusting the level of detail in the generated content according to the importance of the task. Some or all of the above processing in the generation unit is performed using the generation AI. For example, the generation unit can input data on the importance of the freelancer's work into the generation AI and have the generation AI adjust the level of detail in the generated content.

[0077] The generation unit can apply different generation algorithms depending on the freelancer's work category during generation. For example, the generation unit applies an efficient schedule generation algorithm to scheduling tasks. For example, it applies a reply content generation algorithm using natural language processing to email reply tasks. The generation unit can also apply a template-based generation algorithm to document creation tasks. In this way, the generation unit can efficiently perform tasks by applying the appropriate generation algorithm according to the work category. Some or all of the above processing in the generation unit is performed using a generation AI. For example, the generation unit can input freelancer work category data into the generation AI and have the generation AI execute the application of the generation algorithm.

[0078] The generation unit can estimate the freelancer's emotions and adjust the content of the email replies it generates based on those estimated emotions. For example, if the freelancer is stressed, the generation unit's AI will create a concise and to-the-point reply. If the freelancer is relaxed, the generation unit's AI will create a reply that includes detailed information. Furthermore, if the freelancer is busy, the generation unit's AI can create a short reply that allows for a quick response. This allows the generation unit to generate more appropriate replies by adjusting them according to the freelancer's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processes in the generation unit are performed using the generation AI. For example, the generation unit can input the freelancer's emotion data into the generation AI and have the generation AI adjust the content of the email replies.

[0079] The generation unit can determine the priority of generated content based on the submission deadlines of freelancers' work during the generation process. For example, the generation unit will prioritize generating content for tasks with approaching deadlines. For example, it will postpone generating content for tasks with distant submission deadlines. The generation unit can also adjust the level of detail and priority of the generated content according to the submission deadline. This allows the generation unit to efficiently perform its tasks by determining the priority of generated content according to the submission deadline. Some or all of the above processing in the generation unit is performed using a generation AI. For example, the generation unit can input data on the submission deadlines of freelancers' work into the generation AI and have the generation AI determine the priority of the generated content.

[0080] The generation unit can adjust the order of generated content based on the relevance of the freelancer's tasks during generation. For example, the generation unit can prioritize generating content for highly relevant tasks. For example, it can postpone generating content for less relevant tasks. The generation unit can also adjust the order and level of detail of the generated content according to the relevance of the tasks. This allows the generation unit to efficiently perform tasks by adjusting the order of generated content according to the relevance of the tasks. Some or all of the above processing in the generation unit is performed using a generation AI. For example, the generation unit can input data on the relevance of the freelancer's tasks into the generation AI and have the generation AI perform the adjustment of the order of generated content.

[0081] The service provider can estimate the freelancer's emotions and adjust the support provided based on those emotions. For example, if the freelancer is stressed, the service provider can provide quick and concise support. If the freelancer is relaxed, the service provider can provide detailed explanations and support. The service provider can also provide support that can be completed in a short time if the freelancer is busy. This allows the service provider to provide more appropriate support by adjusting the support method according to the freelancer's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the service provider may be performed using AI or not. For example, the service provider can input the freelancer's emotion data into a generative AI and have the generative AI adjust the support method.

[0082] The service provider can analyze the freelancer's past support history to select the optimal support method at the time of service provision. For example, the service provider can analyze the support the freelancer has received in the past and provide similar support. For example, the service provider can select an effective support method from the freelancer's past support history. The service provider can also propose the optimal support method based on the freelancer's past support history. In this way, the service provider can select the optimal support method by analyzing past support history and provide support efficiently. Some or all of the above processes in the service provider may be performed using AI or not. For example, the service provider can input the freelancer's past support history data into a generating AI and have the generating AI select the optimal support method.

[0083] The service provider can customize the means of support based on the freelancer's current work situation at the time of provision. For example, the service provider can provide support related to the project the freelancer is currently working on. For example, the service provider can customize the necessary means of support according to the freelancer's current work situation. The service provider can also propose the most suitable support method considering the freelancer's current work situation. In this way, the service provider can provide more appropriate support by customizing the means of support based on the current work situation. Some or all of the above processing in the service provider may be performed using AI or not. For example, the service provider can input data on the freelancer's current work situation into a generating AI and have the generating AI perform the customization of the means of support.

[0084] The service provider can estimate the freelancer's emotions and prioritize the support provided based on those emotions. For example, if the freelancer is stressed, the service provider will prioritize high-priority support. If the freelancer is relaxed, the service provider will provide detailed support. Furthermore, if the freelancer is busy, the service provider can prioritize support that can be completed quickly. This allows the service provider to prioritize important support by determining support priorities according to the freelancer's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the service provider may be performed using AI or not. For example, the service provider can input the freelancer's emotion data into a generative AI and have the generative AI determine the priority of support.

[0085] The service provider can select the optimal support method by considering the freelancer's geographical location information at the time of service provision. For example, if the freelancer is working in a specific region, the service provider will provide support relevant to that region. For example, based on the freelancer's geographical location information, the service provider will provide support that addresses region-specific needs and challenges. Furthermore, if the freelancer is on the move, the service provider can provide support relevant to their current location in real time. In this way, the service provider can provide support that addresses region-specific needs and challenges by considering geographical location information. Some or all of the above processing in the service provider may be performed using AI or not. For example, the service provider can input the freelancer's geographical location information into a generating AI and have the generating AI select the optimal support method.

[0086] The service provider can analyze a freelancer's social media activity and propose support measures at the time of service provision. For example, the service provider can analyze a freelancer's social media posts and provide support related to topics of interest. For example, the service provider can analyze a freelancer's followers and network and provide support related to relevant industries and fields. The service provider can also provide support regarding current trends and topics based on the freelancer's social media activity. In this way, the service provider can provide support regarding topics of interest and current trends by analyzing social media activity. Some or all of the above processing in the service provider may be performed using AI or not. For example, the service provider can input freelancer social media activity data into a generating AI and have the generating AI propose support measures.

[0087] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0088] The data collection department can analyze freelancers' social media activity to understand their work style and needs. For example, it can analyze freelancers' social media posts to identify topics of interest and current trends. It can also analyze freelancers' followers and networks to gather information on relevant industries and fields. Furthermore, it can gather information on current projects and areas of interest from freelancers' social media activity. This allows the data collection department to gain a more accurate understanding of freelancers' work style and needs through their social media activity.

[0089] The generation unit can estimate the freelancer's emotions and adjust how it proposes schedules based on those emotions. For example, if the freelancer is stressed, the generation unit will propose a schedule with more leeway. If the freelancer is relaxed, the generation unit will propose an efficient schedule. Furthermore, if the freelancer is busy, the generation unit can propose a schedule that prioritizes important tasks. In this way, the generation unit can propose a more appropriate schedule by adjusting how it proposes schedules according to the freelancer's emotions.

[0090] The service provider can estimate the freelancer's emotions and adjust the support they provide based on those estimates. For example, if the freelancer is stressed, the service provider will provide quick and concise support. If the freelancer is relaxed, the service provider will provide more detailed explanations and support. Furthermore, if the freelancer is busy, the service provider can provide support that can be completed in a short amount of time. This allows the service provider to provide more appropriate support by adjusting their approach according to the freelancer's emotions.

[0091] The data collection unit can prioritize collecting highly relevant information by considering the geographical location of freelancers. For example, if a freelancer is working in a specific region, the unit will prioritize collecting information related to that region. Based on the freelancer's geographical location, the unit collects information on region-specific needs and challenges. Furthermore, if a freelancer is on the move, the unit can collect information related to their current location in real time. This allows the unit to efficiently collect information on region-specific needs and challenges by considering geographical location.

[0092] The generation unit can adjust the level of detail in the generated content based on the importance of the freelancer's work. For example, the generation unit generates a detailed schedule for high-priority tasks and a simplified schedule for low-priority tasks. Furthermore, the generation unit can generate schedules that take into account the necessary resources and time, depending on the importance of the task. This allows the generation unit to efficiently execute tasks by adjusting the level of detail in the generated content according to the importance of the task.

[0093] The service provider can analyze the freelancer's past support history to select the most suitable support method at the time of provision. For example, the service provider can analyze the support the freelancer has received in the past and provide similar support. The service provider selects an effective support method based on the freelancer's past support history. Furthermore, the service provider can propose the most suitable support method based on the freelancer's past support history. In this way, the service provider can select the most suitable support method by analyzing past support history and provide support efficiently.

[0094] The data collection team can estimate the freelancer's emotions and adjust the content of surveys and interviews based on those estimates. For example, if a freelancer is stressed, the team will prioritize creating surveys with concise and easy-to-answer questions. If a freelancer is relaxed, the team will increase the number of open-ended questions to elicit more detailed information. Furthermore, if a freelancer is busy, the team can reduce the number of questions and increase the number of options to allow for quicker responses. This allows the team to collect more relevant information by adjusting questions according to the freelancer's emotions.

[0095] The generation unit can apply different generation algorithms depending on the freelancer's work category during the generation process. For example, the generation unit applies an efficient schedule generation algorithm to scheduling tasks. For email reply tasks, it applies a reply content generation algorithm using natural language processing. Furthermore, the generation unit can apply a template-based generation algorithm to document creation tasks. This allows the generation unit to efficiently perform tasks by applying the appropriate generation algorithm according to the work category.

[0096] The service provider can customize the support provided based on the freelancer's current work situation. For example, the service provider can provide support related to the project the freelancer is currently working on. The service provider customizes the necessary support methods according to the freelancer's current work situation. The service provider can also propose the most suitable support method considering the freelancer's current work situation. In this way, the service provider can provide more appropriate support by customizing the support methods based on the current work situation.

[0097] The service provider can estimate the freelancer's emotions and prioritize the support they provide based on those estimates. For example, if the freelancer is stressed, the service provider will prioritize high-priority support. If the freelancer is relaxed, the service provider will provide detailed support. Furthermore, if the freelancer is busy, the service provider can prioritize support that can be completed quickly. This allows the service provider to prioritize important support by determining support priorities based on the freelancer's emotions.

[0098] The following briefly describes the processing flow for example form 2.

[0099] Step 1: The data collection team understands the freelancers' work styles and needs. For example, the team conducts surveys and interviews, gathering detailed information through online questionnaires and in-person interviews. The team can also analyze the freelancers' work history to select the most suitable data collection method. For example, they can analyze the types and frequency of past projects and include relevant questions in the survey. Step 2: The generation unit efficiently responds to the information collected by the collection unit by utilizing online tools and generation AI. For example, it can use generation AI to analyze a freelancer's calendar and propose an optimal schedule. It can also use generation AI to generate email replies, which can then be sent after confirmation by the freelancer. Step 3: The providing department provides high-quality support based on the information generated by the generating department. For example, they may assign staff with specialized knowledge to provide support tailored to specific tasks. They can also provide support that is compatible with the tools and systems used by freelancers.

[0100] 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.

[0101] 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.

[0102] 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.

[0103] Each of the multiple elements described above, including the collection unit, generation unit, and provision unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the collection unit uses the camera 42 and microphone 38B of the smart device 14 to understand the freelancer's work style and needs. The generation unit is implemented in the specific processing unit 290 of the data processing unit 12 and efficiently responds to the collected information using online tools and generating AI. The provision unit is implemented in the control unit 46A of the smart device 14 and provides high-quality support based on the generated information. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0104] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0105] 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.

[0106] 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.

[0107] 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.

[0108] 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.

[0109] 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).

[0110] 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.

[0111] 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.

[0112] 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.

[0113] 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.

[0114] 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.

[0115] 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.).

[0116] 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.

[0117] 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.

[0118] 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.

[0119] Each of the multiple elements described above, including the collection unit, generation unit, and provision unit, is implemented, for example, in at least one of the smart glasses 214 and the data processing unit 12. For example, the collection unit uses the camera 42 and microphone 238 of the smart glasses 214 to understand the freelancer's work style and needs. The generation unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12, and efficiently responds based on the collected information using online tools and generating AI. The provision unit is implemented, for example, by the control unit 46A of the smart glasses 214, and provides high-quality support based on the generated 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.

[0120] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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).

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.).

[0132] 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.

[0133] 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.

[0134] 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.

[0135] Each of the multiple elements described above, including the collection unit, generation unit, and provision unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the collection unit uses the camera 42 and microphone 238 of the headset terminal 314 to understand the freelancer's work style and needs. The generation unit is implemented in the specific processing unit 290 of the data processing unit 12 and efficiently responds to the collected information using online tools and generating AI. The provision unit is implemented in the control unit 46A of the headset terminal 314 and provides high-quality support based on the generated information. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0136] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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).

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.).

[0149] 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.

[0150] 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.

[0151] 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.

[0152] Each of the multiple elements described above, including the collection unit, generation unit, and provision unit, is implemented, for example, by at least one of the robot 414 and the data processing unit 12. For example, the collection unit uses the camera 42 and microphone 238 of the robot 414 to understand the freelancer's work style and needs. The generation unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, and efficiently responds based on the collected information using online tools and generating AI. The provision unit is implemented, for example, by the control unit 46A of the robot 414, and provides high-quality support based on the generated 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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."

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] (Note 1) A data collection department that understands the work styles and needs of freelancers, Based on the information collected by the aforementioned collection unit, a generation unit efficiently responds by utilizing online tools and generation AI, The system includes a providing unit that provides high-quality support based on the information generated by the generation unit. A system characterized by the following features. (Note 2) The aforementioned collection unit is Conduct surveys and interviews to understand the work styles and needs of freelancers. The system described in Appendix 1, characterized by the features described herein. (Note 3) The generating unit is We use generative AI to analyze freelancers' calendars and suggest the optimal schedule. The system described in Appendix 1, characterized by the features described herein. (Note 4) The generating unit is The system uses AI to generate email replies, which are then reviewed by freelancers before being sent. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned supply unit is, To provide support specifically tailored to particular tasks, we assign staff with specialized knowledge. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned supply unit is, We provide support tailored to the tools and systems used by freelancers. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned collection unit is We estimate the emotions of freelancers and adjust the content of surveys and interviews based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned collection unit is Analyze the past work history of freelancers and select the optimal data collection method. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned collection unit is During collection, filtering is performed based on the freelancer'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 Estimate the emotions of freelancers and prioritize 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 During data collection, the system prioritizes collecting highly relevant information, taking into account the geographical location of freelancers. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned collection unit is During the collection process, we analyze the freelancer's social media activity and gather relevant information. The system described in Appendix 1, characterized by the features described herein. (Note 13) The generating unit is We estimate the emotions of freelancers and adjust the way we propose schedules based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The generating unit is During generation, the level of detail in the generated content is adjusted based on the importance of the freelance work. The system described in Appendix 1, characterized by the features described herein. (Note 15) The generating unit is During generation, different generation algorithms are applied depending on the freelance work category. The system described in Appendix 1, characterized by the features described herein. (Note 16) The generating unit is Estimate the freelancer's emotions and adjust the email replies generated based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The generating unit is During generation, the priority of the generated content is determined based on the submission timing of the freelancer's work. The system described in Appendix 1, characterized by the features described herein. (Note 18) The generating unit is During generation, the order of generated content is adjusted based on the relevance of the freelancer's work. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned supply unit is, We estimate the emotions of freelancers and adjust the support we provide based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned supply unit is, When providing support, we analyze the freelancer's past support history to select the most suitable support method. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned supply unit is, When providing support, we customize the support methods based on the freelancer's current work situation. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned supply unit is, Estimate the freelancer's emotions and prioritize the support provided based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned supply unit is, When providing services, the most suitable support method will be selected, taking into account the freelancer's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned supply unit is, When providing the service, we analyze the freelancer's social media activity and suggest ways to support them. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0172] 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 data collection department that understands the work styles and needs of freelancers, Based on the information collected by the aforementioned collection unit, a generation unit efficiently responds by utilizing online tools and generation AI, The system includes a providing unit that provides high-quality support based on the information generated by the generation unit. A system characterized by the following features.

2. The aforementioned collection unit is Conduct surveys and interviews to understand the work styles and needs of freelancers. The system according to feature 1.

3. The generating unit is We use generative AI to analyze freelancers' calendars and suggest the optimal schedule. The system according to feature 1.

4. The generating unit is The system uses AI to generate email replies, which are then reviewed by freelancers before being sent. The system according to feature 1.

5. The aforementioned supply unit is, To provide support specifically tailored to particular tasks, we assign staff with specialized knowledge. The system according to feature 1.

6. The aforementioned supply unit is, We provide support tailored to the tools and systems used by freelancers. The system according to feature 1.

7. The aforementioned collection unit is We estimate the emotions of freelancers and adjust the content of surveys and interviews based on those estimated emotions. The system according to feature 1.

8. The aforementioned collection unit is Analyze the past work history of freelancers and select the optimal data collection method. The system according to feature 1.

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