system

Abstract

The system according to this embodiment aims to suggest the most suitable job based on the user's work history and skills. [Solution] The system according to this embodiment comprises a reception unit, an analysis unit, and a provision unit. The reception unit receives input of the user's work history and skills. The analysis unit analyzes the data received by the reception unit and proposes the most suitable job to the user. The provision unit provides detailed information of the job proposed by the analysis unit.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, it is difficult to find an optimal job based on a user's work history and skills, and there is room for improvement.

[0005] The system according to the embodiment aims to propose an optimal job based on a user's work history and skills.

Means for Solving the Problems

[0006] The system according to the embodiment includes a reception unit, an analysis unit, and a provision unit. The reception unit receives an input of a user's work history and skills. The analysis unit analyzes the data received by the reception unit and proposes an optimal job for the user. The provision unit provides detailed information on the job proposed by the analysis unit.

Effects of the Invention

[0007] The system according to this embodiment can suggest the most suitable job based on the user's work history and skills. [Brief explanation of the drawing]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The daily-labor matching system according to an embodiment of the present invention is a system that utilizes generative AI to target all age groups seeking daily-labor work. This system aims to solve the problem of the time it takes to find daily-labor work. With conventional methods, it took a lot of time and effort to find a part-time job for tomorrow, but by utilizing generative AI, it becomes possible to find a job for tomorrow with a single search. Users can easily find a part-time job for tomorrow using the search function provided by the generative AI. It also solves the problem of not being able to find work that matches one's work history. The generative AI analyzes the user's work history data and proposes the most suitable job for the user. This makes it easy for users to find work that matches their work history. Furthermore, it solves the problem of not knowing what kind of work will help improve one's skills. The generative AI analyzes the user's skill data and proposes jobs that will help improve skills. This makes it possible for users to find work that will help improve their skills. Specifically, the user first inputs their work history and skills into the generative AI. Next, the generative AI analyzes the input data and proposes the most suitable job for the user. The user then selects a job that suits them from the proposed jobs. Finally, the generating AI provides detailed information about the selected jobs and supports the user in the job application process. In this way, the day-labor matching system utilizing generating AI solves problems such as finding day-labor jobs, discovering jobs that match one's work history, and being offered jobs that lead to skill development, providing a convenient and efficient service for users. As a result, the day-labor matching system enables efficient day-labor matching by suggesting the most suitable jobs based on the user's work history and skills and providing detailed information.

[0029] The daily-wage matching system according to this embodiment comprises a reception unit, an analysis unit, and a provision unit. The reception unit accepts input of the user's work history and skills. The reception unit provides, for example, an interface for the user to input their work history and skills. The reception unit can also automatically display the work history and skills previously entered by the user as candidates. The reception unit can also prioritize suggesting input methods (voice, text, etc.) previously used by the user. The analysis unit uses a generation AI to analyze the data received by the reception unit and proposes the most suitable job to the user. The analysis unit, for example, analyzes the user's work history data and proposes the most suitable job to the user. The analysis unit can also analyze the user's skills data and propose jobs that will lead to skill improvement. The analysis unit uses a generation AI to analyze the user's work history data and skills data and propose the most suitable job. The provision unit provides detailed information of the jobs proposed by the analysis unit. The provision unit displays, for example, detailed information of the job selected by the user. The provision unit can also support the user in the process of applying for a job. The provision unit provides detailed information of the job selected by the user and supports the application process. As a result, the day-labor matching system according to this embodiment enables efficient day-labor matching by suggesting the most suitable jobs based on the user's work history and skills and providing detailed information.

[0030] The reception desk accepts user input of work history and skills. For example, the reception desk provides an interface for users to input their work history and skills. Specifically, it provides a form where users can input details of their past job titles, duties, and acquired qualifications and skills. The interface includes categorization of work history and skills, as well as input assistance functions, to make input easier for users. For example, when entering work history, items such as company name, position, employment period, and duties are automatically displayed to allow users to easily input the information. Similarly, when entering skills, specific skills such as programming languages, tools, and software are presented as options to allow users to accurately input their skills. Furthermore, the reception desk can automatically display previously entered work history and skills as suggestions. This allows users to reuse past input and save time. For example, previously entered work history and skills are stored in a database and automatically displayed as suggestions when new information is entered. The reception desk can also prioritize suggesting input methods previously used by the user (voice, text, etc.). For example, if a user prefers voice input, the voice input interface is prioritized to allow for smoother input. This allows the reception desk to improve the user input experience and achieve efficient data collection.

[0031] The analysis department uses generative AI to analyze data received by the reception department and propose the most suitable jobs to users. Specifically, the generative AI analyzes the user's work history and skills data in detail to find the job that best suits the user's experience and skills. For example, the generative AI analyzes the user's work history data and proposes jobs with similar job titles and duties based on past work experience. It can also analyze the user's skills data and propose jobs that match their current skill level or jobs that will help them improve their skills. The generative AI uses natural language processing technology to analyze the text data entered by the user and understand the details of their work history and skills. For example, if a user enters "I have 5 years of experience as a project manager," the generative AI analyzes this information and proposes jobs related to project management. The generative AI also takes into account the user's past application history and evaluation data to make more accurate suggestions. For example, if a user received a high evaluation for a job they applied for in the past, it will prioritize proposing similar jobs. As a result, the analysis department can quickly and accurately propose the most suitable jobs based on the user's work history and skills. Furthermore, the analysis department takes into account factors such as the user's desired conditions, work location, and working hours, and proposes jobs that match the user's needs. This allows the analysis department to increase user satisfaction and achieve efficient matching.

[0032] The provisioning department provides detailed information about jobs suggested by the analysis department. Specifically, it displays detailed information about jobs selected by the user and supports the application process. For example, the provisioning department displays details such as the job title, job description, work location, working hours, salary, and required skills and qualifications. This allows the user to carefully review the suggested job and determine if it is suitable for them. The provisioning department also supports the user in the job application process. For example, it provides an application form, allowing the user to fill in the necessary information and submit their application. Furthermore, the provisioning department displays the progress of the application process in real time, allowing the user to check the status of their application. For example, it provides information such as whether the application has been accepted, the date and location of the interview, and the hiring result. This allows the user to understand the progress of the application process and prepare to move on to the next step. In addition, the provisioning department can improve the accuracy of suggestions by collecting feedback on jobs selected by the user and providing this feedback to the analysis department. For example, it collects feedback on how the user evaluated the suggested jobs, what they liked, and what could be improved, and provides this feedback to the analysis department. This allows the analysis department to improve its suggestion algorithm based on user feedback and make more accurate suggestions. This allows the service provider to provide users with detailed information and support the application process, thereby enabling efficient day labor matching.

[0033] The reception desk can analyze the user's past input history and provide an auto-completion function to reduce input effort. For example, the reception desk can automatically display the user's past work history and skills as suggestions. The reception desk can also prioritize suggesting input methods (voice, text, etc.) that the user has used in the past. The reception desk can also predict and suggest work history and skills to be used at specific times based on the user's past input history. This reduces input effort and enables efficient data entry by utilizing past input history. Some or all of the above processes in the reception desk may be performed using AI, for example, or not using AI. For example, the reception desk can input the user's past input data into a generating AI and have the generating AI execute the auto-completion function.

[0034] The reception system can dynamically change input fields based on the user's current occupation and areas of interest when they input their work history and skills. For example, when a user enters their current occupation, the reception system can automatically display relevant skills and work history input fields based on that information. The reception system can also prioritize displaying relevant work history and skills input fields based on the user's areas of interest. The reception system can also dynamically change input fields based on the user's current occupation and areas of interest to provide the optimal input method. This supports more appropriate data entry by providing input fields that match the user's current situation and interests. Some or all of the above processing in the reception system may be performed using AI, for example, or not using AI. For example, the reception system can input data on the user's current occupation and areas of interest into a generating AI and have the generating AI perform the dynamic changes to the input fields.

[0035] The reception system can prioritize displaying highly relevant input fields when users input their work history and skills, taking into account their geographical location. For example, the reception system can automatically display relevant work history and skill input fields based on the user's current location. The reception system can also prioritize displaying region-specific work history and skill input fields, taking into account the user's geographical location. The reception system can also dynamically change the highly relevant input fields based on the user's geographical location, providing the optimal input method. This supports the input of region-specific work history and skills by considering geographical location. Some or all of the above processing in the reception system may be performed using AI, for example, or without AI. For example, the reception system can input the user's geographical location information into a generating AI and have the generating AI display highly relevant input fields.

[0036] The reception desk can analyze a user's social media activity when they input their work history and skills, and suggest relevant input fields. For example, the reception desk can automatically display relevant work history and skills input fields based on the user's social media activity. The reception desk can also analyze the user's social media activity and prioritize the display of relevant work history and skills input fields. The reception desk can also dynamically change the most relevant input fields based on the user's social media activity and provide the optimal input method. This supports the input of relevant work history and skills by leveraging social media activity. Some or all of the above processing in the reception desk may be performed using AI, for example, or not using AI. For example, the reception desk can input the user's social media activity data into a generating AI and have the generating AI suggest relevant input fields.

[0037] The analysis unit can optimize the analysis algorithm by referring to the user's past work history data during analysis. For example, the analysis unit can select the optimal analysis algorithm based on the user's past work history data. The analysis unit can also dynamically adjust the analysis algorithm by referring to the user's past work history data. The analysis unit can also analyze the user's past work history data and optimize the algorithm to provide the best possible analysis results. In this way, by utilizing past work history data, the analysis algorithm is optimized and highly accurate analysis results are provided. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the user's past work history data into a generating AI and have the generating AI perform the optimization of the analysis algorithm.

[0038] The analysis unit can apply different analysis methods depending on the user's skill level during analysis. For example, the analysis unit can select the optimal analysis method according to the user's skill level. The analysis unit can also dynamically adjust the analysis method based on the user's skill level. The analysis unit can also apply different analysis methods according to the user's skill level to provide the optimal analysis result. In this way, by providing an analysis method appropriate to the skill level, the optimal analysis result is provided. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without using AI. For example, the analysis unit can input user skill level data into a generating AI and have the generating AI execute the application of the analysis method.

[0039] The analysis unit can propose the most suitable job by considering the user's geographical location information during analysis. For example, the analysis unit proposes the most suitable job based on the user's current location. The analysis unit can also propose region-specific jobs by considering the user's geographical location information. The analysis unit can also dynamically propose the most suitable job based on the user's geographical location information. This allows for the proposal of region-specific jobs by considering geographical location information. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the user's geographical location information into a generating AI and have the generating AI execute the optimal job proposal.

[0040] The analysis unit can analyze a user's social media activity during analysis and suggest relevant jobs. For example, the analysis unit can suggest relevant jobs based on the user's social media activity. The analysis unit can also analyze a user's social media activity and prioritize suggesting relevant jobs. The analysis unit can also dynamically suggest the most suitable jobs based on the user's social media activity. In this way, relevant jobs are suggested by leveraging social media activity. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the user's social media activity data into a generating AI and have the generating AI generate suggestions for relevant jobs.

[0041] The information delivery unit can select the optimal information delivery method by referring to the user's past application history at the time of delivery. For example, the information delivery unit selects the optimal information delivery method based on the user's past application history. The information delivery unit can also dynamically adjust the information delivery method by referring to the user's past application history. The information delivery unit can also analyze the user's past application history and provide the optimal information delivery method. In this way, the optimal information delivery method is provided by utilizing past application history. Some or all of the above processing in the information delivery unit may be performed using AI, for example, or without using AI. For example, the information delivery unit can input the user's past application history data into a generating AI and have the generating AI perform the selection of the information delivery method.

[0042] The information delivery unit can customize the means of information delivery based on the user's current living situation at the time of delivery. For example, the information delivery unit can select the optimal means of information delivery based on the user's current living situation. The information delivery unit can also dynamically adjust the means of information delivery considering the user's living situation. The information delivery unit can also provide the optimal means of information delivery based on the user's current living situation. This improves the user experience by providing means of information delivery that are appropriate to the current living situation. Some or all of the above processing in the information delivery unit may be performed using AI, for example, or without using AI. For example, the information delivery unit can input user living situation data into a generating AI and have the generating AI perform the customization of the means of information delivery.

[0043] The information delivery unit can select the optimal information delivery method at the time of delivery, taking into account the user's geographical location information. For example, the information delivery unit can select the optimal information delivery method based on the user's current location. The information delivery unit can also select a region-specific information delivery method, taking into account the user's geographical location information. The information delivery unit can also dynamically select the optimal information delivery method based on the user's geographical location information. This allows for the provision of region-specific information delivery methods by considering geographical location information. Some or all of the above-described processing in the information delivery unit may be performed using AI, for example, or without AI. For example, the information delivery unit can input the user's geographical location information into a generating AI and have the generating AI perform the selection of the information delivery method.

[0044] The service provider can analyze the user's social media activity and provide relevant information at the time of delivery. For example, the service provider can provide relevant information based on the user's social media activity. The service provider can also analyze the user's social media activity and prioritize the provision of relevant information. The service provider can also dynamically provide optimal information based on the user's social media activity. In this way, relevant information is provided by leveraging social media activity. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the user's social media activity data into a generating AI and have the generating AI perform the provision of relevant information.

[0045] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0046] The day-labor matching system can also include a health monitoring unit that monitors the user's health status and suggests the most suitable jobs. For example, the health monitoring unit measures the user's heart rate and stress level and suggests appropriate jobs based on this data. If the user is fatigued, it can prioritize suggesting light work; conversely, if the user is energetic, it can suggest physically demanding jobs. Furthermore, the health monitoring unit can analyze the user's sleep data and suggest suitable jobs after sufficient rest. This enables job suggestions tailored to the user's health condition, supporting their well-being.

[0047] The day-labor matching system can also include a hobby analysis department that suggests jobs based on the user's hobbies and interests. For example, if a user is interested in music, the hobby analysis department can suggest music-related jobs. If a user is interested in sports, it can suggest jobs such as staffing at sports events. Furthermore, if a user is interested in cooking, the hobby analysis department can suggest jobs in restaurants. This allows for job suggestions tailored to the user's hobbies and interests, improving job satisfaction.

[0048] The daily-wage matching system can also include an evaluation analysis unit that analyzes users' past evaluation data and proposes the most suitable jobs. For example, the evaluation analysis unit can prioritize jobs where the user has received high ratings in the past. It can also suggest jobs where the user has received low ratings in the past. Furthermore, based on the user's evaluation data, the evaluation analysis unit can suggest jobs where skill development is expected. This enables optimal job suggestions based on the user's past evaluations, supporting the user's growth.

[0049] The daily-wage matching system can also include a learning analysis unit that analyzes the user's learning history and proposes jobs that will lead to skill improvement. For example, the learning analysis unit can propose jobs that utilize the skills the user has learned in the past. It can also propose jobs related to skills the user wants to learn. Furthermore, based on the user's learning history, the learning analysis unit can propose jobs that are expected to improve their skills. This enables optimal job proposals based on the user's learning history, supporting the user's growth.

[0050] The day-labor matching system can also include an application analysis unit that analyzes the user's past application history and proposes the optimal application method. For example, the application analysis unit can prioritize suggesting application methods that the user has successfully used in the past. It can also suggest methods to avoid that the user has failed at in the past. Furthermore, based on the user's application history, the application analysis unit can suggest the optimal timing for application. This enables the system to propose the most suitable application method based on the user's past application history, thereby improving the application success rate.

[0051] The day-labor matching system can also include a commute analysis unit that analyzes the user's geographical location information and proposes the optimal commuting method. For example, the commute analysis unit can suggest the most efficient commuting method from the user's current location. It can also suggest the optimal route if the user uses public transportation. Furthermore, the commute analysis unit can suggest the optimal route if the user commutes by bicycle or on foot. This enables the suggestion of the optimal commuting method based on the user's geographical location information, thereby reducing the burden of commuting.

[0052] The following briefly describes the processing flow for example form 1.

[0053] Step 1: The reception desk receives the user's work history and skills. The reception desk provides, for example, an interface for the user to enter their work history and skills. Furthermore, the reception desk can automatically display the work history and skills the user has entered in the past as suggestions and can prioritize suggesting input methods the user has used in the past (voice, text, etc.). Step 2: The analysis unit uses generation AI to analyze the data received by the reception unit and proposes the most suitable job for the user. For example, the analysis unit analyzes the user's work history data and skill data to propose the most suitable job or a job that will help the user improve their skills. Step 3: The provisioning unit provides detailed information about the jobs proposed by the analysis unit. For example, the provisioning unit displays detailed information about the jobs selected by the user and supports the user in the process of applying for the jobs.

[0054] (Example of form 2) The daily-labor matching system according to an embodiment of the present invention is a system that utilizes generative AI to target all age groups seeking daily-labor work. This system aims to solve the problem of the time it takes to find daily-labor work. With conventional methods, it took a lot of time and effort to find a part-time job for tomorrow, but by utilizing generative AI, it becomes possible to find a job for tomorrow with a single search. Users can easily find a part-time job for tomorrow using the search function provided by the generative AI. It also solves the problem of not being able to find work that matches one's work history. The generative AI analyzes the user's work history data and proposes the most suitable job for the user. This makes it easy for users to find work that matches their work history. Furthermore, it solves the problem of not knowing what kind of work will help improve one's skills. The generative AI analyzes the user's skill data and proposes jobs that will help improve skills. This makes it possible for users to find work that will help improve their skills. Specifically, the user first inputs their work history and skills into the generative AI. Next, the generative AI analyzes the input data and proposes the most suitable job for the user. The user then selects a job that suits them from the proposed jobs. Finally, the generating AI provides detailed information about the selected jobs and supports the user in the job application process. In this way, the day-labor matching system utilizing generating AI solves problems such as finding day-labor jobs, discovering jobs that match one's work history, and being offered jobs that lead to skill development, providing a convenient and efficient service for users. As a result, the day-labor matching system enables efficient day-labor matching by suggesting the most suitable jobs based on the user's work history and skills and providing detailed information.

[0055] The daily-wage matching system according to this embodiment comprises a reception unit, an analysis unit, and a provision unit. The reception unit accepts input of the user's work history and skills. The reception unit provides, for example, an interface for the user to input their work history and skills. The reception unit can also automatically display the work history and skills previously entered by the user as candidates. The reception unit can also prioritize suggesting input methods (voice, text, etc.) previously used by the user. The analysis unit uses a generation AI to analyze the data received by the reception unit and proposes the most suitable job to the user. The analysis unit, for example, analyzes the user's work history data and proposes the most suitable job to the user. The analysis unit can also analyze the user's skills data and propose jobs that will lead to skill improvement. The analysis unit uses a generation AI to analyze the user's work history data and skills data and propose the most suitable job. The provision unit provides detailed information of the jobs proposed by the analysis unit. The provision unit displays, for example, detailed information of the job selected by the user. The provision unit can also support the user in the process of applying for a job. The provision unit provides detailed information of the job selected by the user and supports the application process. As a result, the day-labor matching system according to this embodiment enables efficient day-labor matching by suggesting the most suitable jobs based on the user's work history and skills and providing detailed information.

[0056] The reception desk accepts user input of work history and skills. For example, the reception desk provides an interface for users to input their work history and skills. Specifically, it provides a form where users can input details of their past job titles, duties, and acquired qualifications and skills. The interface includes categorization of work history and skills, as well as input assistance functions, to make input easier for users. For example, when entering work history, items such as company name, position, employment period, and duties are automatically displayed to allow users to easily input the information. Similarly, when entering skills, specific skills such as programming languages, tools, and software are presented as options to allow users to accurately input their skills. Furthermore, the reception desk can automatically display previously entered work history and skills as suggestions. This allows users to reuse past input and save time. For example, previously entered work history and skills are stored in a database and automatically displayed as suggestions when new information is entered. The reception desk can also prioritize suggesting input methods previously used by the user (voice, text, etc.). For example, if a user prefers voice input, the voice input interface is prioritized to allow for smoother input. This allows the reception desk to improve the user input experience and achieve efficient data collection.

[0057] The analysis department uses generative AI to analyze data received by the reception department and propose the most suitable jobs to users. Specifically, the generative AI analyzes the user's work history and skills data in detail to find the job that best suits the user's experience and skills. For example, the generative AI analyzes the user's work history data and proposes jobs with similar job titles and duties based on past work experience. It can also analyze the user's skills data and propose jobs that match their current skill level or jobs that will help them improve their skills. The generative AI uses natural language processing technology to analyze the text data entered by the user and understand the details of their work history and skills. For example, if a user enters "I have 5 years of experience as a project manager," the generative AI analyzes this information and proposes jobs related to project management. The generative AI also takes into account the user's past application history and evaluation data to make more accurate suggestions. For example, if a user received a high evaluation for a job they applied for in the past, it will prioritize proposing similar jobs. As a result, the analysis department can quickly and accurately propose the most suitable jobs based on the user's work history and skills. Furthermore, the analysis department takes into account factors such as the user's desired conditions, work location, and working hours, and proposes jobs that match the user's needs. This allows the analysis department to increase user satisfaction and achieve efficient matching.

[0058] The provisioning department provides detailed information about jobs suggested by the analysis department. Specifically, it displays detailed information about jobs selected by the user and supports the application process. For example, the provisioning department displays details such as the job title, job description, work location, working hours, salary, and required skills and qualifications. This allows the user to carefully review the suggested job and determine if it is suitable for them. The provisioning department also supports the user in the job application process. For example, it provides an application form, allowing the user to fill in the necessary information and submit their application. Furthermore, the provisioning department displays the progress of the application process in real time, allowing the user to check the status of their application. For example, it provides information such as whether the application has been accepted, the date and location of the interview, and the hiring result. This allows the user to understand the progress of the application process and prepare to move on to the next step. In addition, the provisioning department can improve the accuracy of suggestions by collecting feedback on jobs selected by the user and providing this feedback to the analysis department. For example, it collects feedback on how the user evaluated the suggested jobs, what they liked, and what could be improved, and provides this feedback to the analysis department. This allows the analysis department to improve its suggestion algorithm based on user feedback and make more accurate suggestions. This allows the service provider to provide users with detailed information and support the application process, thereby enabling efficient day labor matching.

[0059] The reception desk can estimate the user's emotions and customize the input interface for work history and skills based on the estimated emotions. For example, if the user is stressed, the reception desk can provide a simple and intuitive interface and minimize the input steps. If the user is relaxed, the reception desk can also provide detailed input options and suggest customizable input methods. If the user is in a hurry, the reception desk can prioritize voice input to allow for quick input of work history and skills. This reduces the effort required for input and improves the user experience by providing an interface that responds to the user's emotions. Emotion estimation is achieved using emotion estimation functions, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception desk may be performed using AI or not. For example, the reception desk can input the user's facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0060] The reception desk can analyze the user's past input history and provide an auto-completion function to reduce input effort. For example, the reception desk can automatically display the user's past work history and skills as suggestions. The reception desk can also prioritize suggesting input methods (voice, text, etc.) that the user has used in the past. The reception desk can also predict and suggest work history and skills to be used at specific times based on the user's past input history. This reduces input effort and enables efficient data entry by utilizing past input history. Some or all of the above processes in the reception desk may be performed using AI, for example, or not using AI. For example, the reception desk can input the user's past input data into a generating AI and have the generating AI execute the auto-completion function.

[0061] The reception system can dynamically change input fields based on the user's current occupation and areas of interest when they input their work history and skills. For example, when a user enters their current occupation, the reception system can automatically display relevant skills and work history input fields based on that information. The reception system can also prioritize displaying relevant work history and skills input fields based on the user's areas of interest. The reception system can also dynamically change input fields based on the user's current occupation and areas of interest to provide the optimal input method. This supports more appropriate data entry by providing input fields that match the user's current situation and interests. Some or all of the above processing in the reception system may be performed using AI, for example, or not using AI. For example, the reception system can input data on the user's current occupation and areas of interest into a generating AI and have the generating AI perform the dynamic changes to the input fields.

[0062] The reception desk can estimate the user's emotions and determine input priorities based on the estimated emotions. For example, if the user is stressed, the reception desk may prioritize displaying important input fields and simplify the input process. If the user is relaxed, the reception desk may also provide detailed input fields and suggest customizable input methods. If the user is in a hurry, the reception desk may prioritize displaying the most important input fields to enable quick input. This enables efficient data entry by providing input priorities according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI 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 reception desk may be performed using AI or not using AI. For example, the reception desk may input the user's facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0063] The reception system can prioritize displaying highly relevant input fields when users input their work history and skills, taking into account their geographical location. For example, the reception system can automatically display relevant work history and skill input fields based on the user's current location. The reception system can also prioritize displaying region-specific work history and skill input fields, taking into account the user's geographical location. The reception system can also dynamically change the highly relevant input fields based on the user's geographical location, providing the optimal input method. This supports the input of region-specific work history and skills by considering geographical location. Some or all of the above processing in the reception system may be performed using AI, for example, or without AI. For example, the reception system can input the user's geographical location information into a generating AI and have the generating AI display highly relevant input fields.

[0064] The reception desk can analyze a user's social media activity when they input their work history and skills, and suggest relevant input fields. For example, the reception desk can automatically display relevant work history and skills input fields based on the user's social media activity. The reception desk can also analyze the user's social media activity and prioritize the display of relevant work history and skills input fields. The reception desk can also dynamically change the most relevant input fields based on the user's social media activity and provide the optimal input method. This supports the input of relevant work history and skills by leveraging social media activity. Some or all of the above processing in the reception desk may be performed using AI, for example, or not using AI. For example, the reception desk can input the user's social media activity data into a generating AI and have the generating AI suggest relevant input fields.

[0065] The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated user emotions. For example, if the user is relaxed, the analysis unit can display detailed analysis results and provide a customizable display method. If the user is in a hurry, the analysis unit can also display concise analysis results that get straight to the point. If the user is stressed, the analysis unit can provide a visually easy-to-understand display method and simplify the analysis results. This improves the user experience by providing a display method of analysis results that is tailored to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI, for example, or not using AI. For example, the analysis unit can input the user's facial expression data into the generative AI and have the generative AI perform emotion estimation.

[0066] The analysis unit can optimize the analysis algorithm by referring to the user's past work history data during analysis. For example, the analysis unit can select the optimal analysis algorithm based on the user's past work history data. The analysis unit can also dynamically adjust the analysis algorithm by referring to the user's past work history data. The analysis unit can also analyze the user's past work history data and optimize the algorithm to provide the best possible analysis results. In this way, by utilizing past work history data, the analysis algorithm is optimized and highly accurate analysis results are provided. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the user's past work history data into a generating AI and have the generating AI perform the optimization of the analysis algorithm.

[0067] The analysis unit can apply different analysis methods depending on the user's skill level during analysis. For example, the analysis unit can select the optimal analysis method according to the user's skill level. The analysis unit can also dynamically adjust the analysis method based on the user's skill level. The analysis unit can also apply different analysis methods according to the user's skill level to provide the optimal analysis result. In this way, by providing an analysis method appropriate to the skill level, the optimal analysis result is provided. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without using AI. For example, the analysis unit can input user skill level data into a generating AI and have the generating AI execute the application of the analysis method.

[0068] The analysis unit can estimate the user's emotions and determine the priority of analysis results based on the estimated emotions. For example, if the user is relaxed, the analysis unit will prioritize displaying detailed analysis results. If the user is in a hurry, the analysis unit can also prioritize displaying concise analysis results. If the user is stressed, the analysis unit can also prioritize displaying visually easy-to-understand analysis results. This enables efficient information delivery by providing priority of analysis results according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the user's facial expression data into the generative AI and have the generative AI perform emotion estimation.

[0069] The analysis unit can propose the most suitable job by considering the user's geographical location information during analysis. For example, the analysis unit proposes the most suitable job based on the user's current location. The analysis unit can also propose region-specific jobs by considering the user's geographical location information. The analysis unit can also dynamically propose the most suitable job based on the user's geographical location information. This allows for the proposal of region-specific jobs by considering geographical location information. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the user's geographical location information into a generating AI and have the generating AI execute the optimal job proposal.

[0070] The analysis unit can analyze a user's social media activity during analysis and suggest relevant jobs. For example, the analysis unit can suggest relevant jobs based on the user's social media activity. The analysis unit can also analyze a user's social media activity and prioritize suggesting relevant jobs. The analysis unit can also dynamically suggest the most suitable jobs based on the user's social media activity. In this way, relevant jobs are suggested by leveraging social media activity. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the user's social media activity data into a generating AI and have the generating AI generate suggestions for relevant jobs.

[0071] The service provider can estimate the user's emotions and adjust how detailed job information is displayed based on the estimated emotions. For example, if the user is relaxed, the service provider can display detailed job information and provide a customizable display method. If the user is in a hurry, the service provider can also display concise job information that gets straight to the point. If the user is stressed, the service provider can provide a visually easy-to-understand display method and simplify the job information. This improves the user experience by providing a display method that responds to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the service provider may be performed using AI or not using AI. For example, the service provider can input user facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0072] The information delivery unit can select the optimal information delivery method by referring to the user's past application history at the time of delivery. For example, the information delivery unit selects the optimal information delivery method based on the user's past application history. The information delivery unit can also dynamically adjust the information delivery method by referring to the user's past application history. The information delivery unit can also analyze the user's past application history and provide the optimal information delivery method. In this way, the optimal information delivery method is provided by utilizing past application history. Some or all of the above processing in the information delivery unit may be performed using AI, for example, or without using AI. For example, the information delivery unit can input the user's past application history data into a generating AI and have the generating AI perform the selection of the information delivery method.

[0073] The information delivery unit can customize the means of information delivery based on the user's current living situation at the time of delivery. For example, the information delivery unit can select the optimal means of information delivery based on the user's current living situation. The information delivery unit can also dynamically adjust the means of information delivery considering the user's living situation. The information delivery unit can also provide the optimal means of information delivery based on the user's current living situation. This improves the user experience by providing means of information delivery that are appropriate to the current living situation. Some or all of the above processing in the information delivery unit may be performed using AI, for example, or without using AI. For example, the information delivery unit can input user living situation data into a generating AI and have the generating AI perform the customization of the means of information delivery.

[0074] The information provider can estimate the user's emotions and determine the priority of information provision based on the estimated emotions. For example, if the user is relaxed, the information provider may prioritize providing detailed information. If the user is in a hurry, the information provider may prioritize providing concise information. If the user is stressed, the information provider may prioritize providing visually easy-to-understand information. This enables efficient information provision by providing information priorities according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI 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 information provider may be performed using AI, for example, or not using AI. For example, the information provider can input user facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0075] The information delivery unit can select the optimal information delivery method at the time of delivery, taking into account the user's geographical location information. For example, the information delivery unit can select the optimal information delivery method based on the user's current location. The information delivery unit can also select a region-specific information delivery method, taking into account the user's geographical location information. The information delivery unit can also dynamically select the optimal information delivery method based on the user's geographical location information. This allows for the provision of region-specific information delivery methods by considering geographical location information. Some or all of the above-described processing in the information delivery unit may be performed using AI, for example, or without AI. For example, the information delivery unit can input the user's geographical location information into a generating AI and have the generating AI perform the selection of the information delivery method.

[0076] The service provider can analyze the user's social media activity and provide relevant information at the time of delivery. For example, the service provider can provide relevant information based on the user's social media activity. The service provider can also analyze the user's social media activity and prioritize the provision of relevant information. The service provider can also dynamically provide optimal information based on the user's social media activity. In this way, relevant information is provided by leveraging social media activity. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the user's social media activity data into a generating AI and have the generating AI perform the provision of relevant information.

[0077] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0078] The day-labor matching system can also include a health monitoring unit that monitors the user's health status and suggests the most suitable jobs. For example, the health monitoring unit measures the user's heart rate and stress level and suggests appropriate jobs based on this data. If the user is fatigued, it can prioritize suggesting light work; conversely, if the user is energetic, it can suggest physically demanding jobs. Furthermore, the health monitoring unit can analyze the user's sleep data and suggest suitable jobs after sufficient rest. This enables job suggestions tailored to the user's health condition, supporting their well-being.

[0079] The day-labor matching system can also be equipped with an emotion analysis unit that estimates the user's emotions and makes job suggestions based on those emotions. For example, if the user is feeling stressed, the emotion analysis unit can suggest jobs in a relaxing environment. If the user is highly motivated, it can also suggest challenging jobs. Furthermore, if the user is feeling anxious, the emotion analysis unit can suggest jobs with a support system in place. This enables optimal job suggestions tailored to the user's emotions, thereby improving user satisfaction.

[0080] The day-labor matching system can also include a hobby analysis department that suggests jobs based on the user's hobbies and interests. For example, if a user is interested in music, the hobby analysis department can suggest music-related jobs. If a user is interested in sports, it can suggest jobs such as staffing at sports events. Furthermore, if a user is interested in cooking, the hobby analysis department can suggest jobs in restaurants. This allows for job suggestions tailored to the user's hobbies and interests, improving job satisfaction.

[0081] The daily-wage matching system can also include an evaluation analysis unit that analyzes users' past evaluation data and proposes the most suitable jobs. For example, the evaluation analysis unit can prioritize jobs where the user has received high ratings in the past. It can also suggest jobs where the user has received low ratings in the past. Furthermore, based on the user's evaluation data, the evaluation analysis unit can suggest jobs where skill development is expected. This enables optimal job suggestions based on the user's past evaluations, supporting the user's growth.

[0082] The day-labor matching system can also include a difficulty adjustment unit that estimates the user's emotions and adjusts the difficulty of the work based on those emotions. For example, if the user is feeling stressed, the difficulty adjustment unit will prioritize suggesting easy jobs. If the user is relaxed, it can also suggest more difficult jobs. Furthermore, if the user is in a hurry, the difficulty adjustment unit can suggest jobs that can be completed in a short time. This makes it possible to adjust the difficulty of the work according to the user's emotions, reducing the burden on the user.

[0083] The daily-wage matching system can also include a learning analysis unit that analyzes the user's learning history and proposes jobs that will lead to skill improvement. For example, the learning analysis unit can propose jobs that utilize the skills the user has learned in the past. It can also propose jobs related to skills the user wants to learn. Furthermore, based on the user's learning history, the learning analysis unit can propose jobs that are expected to improve their skills. This enables optimal job proposals based on the user's learning history, supporting the user's growth.

[0084] The day-labor matching system can also include a reward offering unit that estimates the user's emotions and adjusts the method of offering rewards based on those emotions. For example, if the user is not highly motivated, the reward offering unit will prioritize offering higher-paying jobs. If the user is highly motivated, it can also emphasize other attractive aspects of the job. Furthermore, if the user is feeling stressed, the reward offering unit can offer flexible payment methods. This enables rewards to be offered in accordance with the user's emotions, thereby improving user satisfaction.

[0085] The day-labor matching system can also include an application analysis unit that analyzes the user's past application history and proposes the optimal application method. For example, the application analysis unit can prioritize suggesting application methods that the user has successfully used in the past. It can also suggest methods to avoid that the user has failed at in the past. Furthermore, based on the user's application history, the application analysis unit can suggest the optimal timing for application. This enables the system to propose the most suitable application method based on the user's past application history, thereby improving the application success rate.

[0086] The day-labor matching system can also include a communication adjustment unit that estimates the user's emotions and adjusts the communication method based on those emotions. For example, if the user is stressed, the communication adjustment unit can provide a simple and intuitive communication method. If the user is relaxed, it can also provide detailed information. Furthermore, if the user is in a hurry, the communication adjustment unit can prioritize a quick response. This enables communication methods that are tailored to the user's emotions, improving the user experience.

[0087] The day-labor matching system can also include a commute analysis unit that analyzes the user's geographical location information and proposes the optimal commuting method. For example, the commute analysis unit can suggest the most efficient commuting method from the user's current location. It can also suggest the optimal route if the user uses public transportation. Furthermore, the commute analysis unit can suggest the optimal route if the user commutes by bicycle or on foot. This enables the suggestion of the optimal commuting method based on the user's geographical location information, thereby reducing the burden of commuting.

[0088] The following briefly describes the processing flow for example form 2.

[0089] Step 1: The reception desk receives the user's work history and skills. The reception desk provides, for example, an interface for the user to enter their work history and skills. Furthermore, the reception desk can automatically display the work history and skills the user has entered in the past as suggestions and can prioritize suggesting input methods the user has used in the past (voice, text, etc.). Step 2: The analysis unit uses generation AI to analyze the data received by the reception unit and proposes the most suitable job for the user. For example, the analysis unit analyzes the user's work history data and skill data to propose the most suitable job or a job that will help the user improve their skills. Step 3: The provisioning unit provides detailed information about the jobs proposed by the analysis unit. For example, the provisioning unit displays detailed information about the jobs selected by the user and supports the user in the process of applying for the jobs.

[0090] 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.

[0091] 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.

[0092] 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.

[0093] Each of the multiple elements described above, including the reception unit, analysis unit, and provision unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the smart device 14 and provides an interface for the user to input their work history and skills. The analysis unit is implemented by the identification processing unit 290 of the data processing unit 12 and uses generated AI to analyze the user's work history data and skill data and propose the most suitable job. The provision unit is implemented by the control unit 46A of the smart device 14 and displays detailed information of the job proposed by the analysis unit and supports the user in applying for the job. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0094] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0095] 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.

[0096] 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.

[0097] 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.

[0098] 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.

[0099] 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).

[0100] 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.

[0101] 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.

[0102] 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.

[0103] 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.

[0104] 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.

[0105] 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.).

[0106] 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.

[0107] 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.

[0108] 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.

[0109] Each of the multiple elements described above, including the reception unit, analysis unit, and provision unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the smart glasses 214 and provides an interface for the user to input their work history and skills. The analysis unit is implemented by the identification processing unit 290 of the data processing unit 12 and uses generated AI to analyze the user's work history data and skill data and propose the most suitable job. The provision unit is implemented by the control unit 46A of the smart glasses 214 and displays detailed information of the job proposed by the analysis unit and supports the user in applying for the job. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0110] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0111] 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.

[0112] 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.

[0113] 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.

[0114] 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.

[0115] 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).

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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.).

[0122] 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.

[0123] 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.

[0124] 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.

[0125] Each of the multiple elements described above, including the reception unit, analysis unit, and provision unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the headset terminal 314 and provides an interface for the user to input their work history and skills. The analysis unit is implemented by the identification processing unit 290 of the data processing unit 12 and uses generated AI to analyze the user's work history data and skill data and propose the most suitable job. The provision unit is implemented by the control unit 46A of the headset terminal 314 and displays detailed information of the job proposed by the analysis unit and supports the user in applying for the job. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0126] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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).

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.).

[0139] 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.

[0140] 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.

[0141] 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.

[0142] Each of the multiple elements described above, including the reception unit, analysis unit, and provision unit, is implemented by, for example, at least one of the robot 414 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the robot 414 and provides an interface for the user to input their work history and skills. The analysis unit is implemented by, for example, the identification processing unit 290 of the data processing unit 12 and uses generated AI to analyze the user's work history data and skill data and propose the most suitable job. The provision unit is implemented by, for example, the control unit 46A of the robot 414 and displays detailed information of the job proposed by the analysis unit and supports the user in the process of applying for the job. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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."

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] (Note 1) A reception desk that accepts users' work history and skills, The analysis unit analyzes the data received by the reception unit and proposes the most suitable job to the user, The system comprises a providing unit that provides detailed information about the work proposed by the analysis unit. A system characterized by the following features. (Note 2) The aforementioned reception unit is It estimates the user's emotions and customizes the input interface for work history and skills based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned reception unit is It analyzes the user's past input history and provides an auto-completion function to reduce the effort required for input. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned reception unit is When users enter their work history and skills, the input fields are dynamically changed based on the user's current occupation and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned reception unit is It estimates the user's emotions and determines the priority of inputs based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned reception unit is When users enter their work history and skills, the system prioritizes displaying the most relevant input fields by considering their geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned reception unit is When users enter their work history and skills, the system analyzes their social media activity and suggests relevant input fields. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned analysis unit, It estimates the user's emotions and adjusts how the analysis results are displayed based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned analysis unit, During analysis, the analysis algorithm is optimized by referencing the user's past work history data. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned analysis unit, During analysis, different analysis methods are applied depending on the user's skill level. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned analysis unit, It estimates the user's emotions and prioritizes the analysis results based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned analysis unit, During analysis, the system proposes the most suitable tasks, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned analysis unit, During the analysis, we analyze the user's social media activity and suggest relevant jobs. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned supply unit is, It estimates the user's emotions and adjusts how job details are displayed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned supply unit is, When providing information, the system will refer to the user's past application history to select the most appropriate method of information delivery. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned supply unit is, When providing information, the means of delivery will be customized based on the user's current living situation. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned supply unit is, The system estimates the user's emotions and prioritizes information provision based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned supply unit is, When providing information, the optimal method of information delivery will be selected, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned supply unit is, When providing the service, we analyze the user's social media activity and provide relevant information. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0162] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A reception desk that accepts users' work history and skills, The analysis unit analyzes the data received by the reception unit and proposes the most suitable job to the user, The system comprises a providing unit that provides detailed information about the work proposed by the analysis unit. A system characterized by the following features.

2. The aforementioned reception unit is It estimates the user's emotions and customizes the input interface for work history and skills based on those estimated emotions. The system according to feature 1.

3. The aforementioned reception unit is It analyzes the user's past input history and provides an auto-completion function to reduce the effort required for input. The system according to feature 1.

4. The aforementioned reception unit is When users enter their work history and skills, the input fields are dynamically changed based on the user's current occupation and areas of interest. The system according to feature 1.

5. The aforementioned reception unit is It estimates the user's emotions and determines the priority of inputs based on the estimated user emotions. The system according to feature 1.

6. The aforementioned reception unit is When users enter their work history and skills, the system prioritizes displaying the most relevant input fields by considering their geographical location. The system according to feature 1.

7. The aforementioned reception unit is When users enter their work history and skills, the system analyzes their social media activity and suggests relevant input fields. The system according to feature 1.

8. The aforementioned analysis unit, It estimates the user's emotions and adjusts how the analysis results are displayed based on the estimated emotions. The system according to feature 1.

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