system

Abstract

The system according to this embodiment aims to help users find part-time jobs that match their work history and skills, and to suggest jobs that will help them improve their skills. [Solution] The system according to the embodiment comprises a reception unit, a search unit, an analysis unit, a proposal unit, and a skill proposal unit. The reception unit receives input from the user looking for part-time work. The search unit searches for part-time work information based on the information received by the reception unit. The analysis unit analyzes the user's work history and skills based on the part-time work information found by the search unit. The proposal unit proposes the most suitable part-time work based on the information analyzed by the analysis unit. The skill proposal unit proposes work that will help the user improve their skills based on the part-time work information proposed by the proposal 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, the method including the steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of the chatbot's character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance that responds to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, there is a problem that it is difficult for a user to find a part-time job suitable for their work experience and skills, and it is difficult to find a job that leads to skill improvement.

[0005] The system according to the embodiment aims to enable a user to find a part-time job suitable for their work experience and skills and propose a job that leads to skill improvement.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a reception unit, a search unit, an analysis unit, a proposal unit, and a skill proposal unit. The reception unit receives input from the user regarding their part-time job search. The search unit searches for part-time job information based on the information received by the reception unit. The analysis unit analyzes the user's work history and skills based on the part-time job information retrieved by the search unit. The proposal unit proposes the most suitable part-time job based on the information analyzed by the analysis unit. The skill proposal unit proposes jobs that will help the user improve their skills based on the part-time job information proposed by the proposal unit. [Effects of the Invention]

[0007] The system according to this embodiment can find part-time jobs that match the user's work history and skills, and can also suggest jobs that will help them improve their 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 labeled communication I / F (Interface) is an interface including a communication processor, an antenna, and the like. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The part-time job matching system according to an embodiment of the present invention is a system that utilizes generative AI to streamline the search for part-time jobs and find jobs that match the user's work history and skills. When a user searches for a part-time job, the generative AI suggests the most suitable part-time job with a single search. Next, the generative AI analyzes the user's work history and skills and finds a job that matches them. Furthermore, the generative AI suggests jobs that will help the user improve their skills. For example, if a user enters "I'm looking for a part-time job at a restaurant," the generative AI searches for restaurant part-time job information all at once and suggests the best candidates. This allows the user to obtain a lot of part-time job information in a short time. Next, the generative AI analyzes the user's work history and skills and finds a job that matches them. For example, if a user has experience in the service industry, the generative AI takes that experience into consideration and prioritizes suggesting service industry part-time jobs. This allows the user to find a job that utilizes their experience. Furthermore, the generative AI suggests jobs that will help the user improve their skills. For example, if a user has programming skills, the generative AI suggests IT-related part-time jobs that can utilize those skills. This allows the user to have an opportunity to improve their skills. This system streamlines the part-time job search process, allowing users to find work that matches their work history and skills. Furthermore, by helping users find jobs that contribute to skill development, it also contributes to their career advancement. For example, students can gain practical experience through part-time work, which can be beneficial for their future job search. Thus, the part-time job matching system utilizing generational AI shortens the time spent searching for part-time jobs and helps users find work that matches their work history and skills, making it a beneficial system for users of all ages. In short, the part-time job matching system streamlines the part-time job search process and helps users find work that matches their work history and skills.

[0029] The part-time job matching system according to this embodiment comprises a reception unit, a search unit, an analysis unit, a suggestion unit, and a skill suggestion unit. The reception unit receives input from the user regarding their part-time job search. The reception unit provides, for example, an interface for the user to input keywords and conditions when searching for a part-time job. The reception unit receives the information entered by the user and passes it on to the next process. The search unit searches for part-time job information based on the information received by the reception unit. The search unit searches for the most suitable part-time job information based on the user's input, for example, using a generation AI. The search unit refers to a database of part-time job information and finds part-time jobs that match the user's conditions. For example, if the user enters "I'm looking for a part-time job at a restaurant," the search unit searches for restaurant part-time job information all at once and suggests the most suitable candidates. The analysis unit analyzes the user's work history and skills based on the part-time job information retrieved by the search unit. The analysis unit analyzes the user's work history and skills, for example, using a generation AI, and finds jobs that match them. The analysis unit considers the user's past work history and skills and provides information to suggest the most suitable part-time job. The Proposal Unit proposes the most suitable part-time jobs based on the information analyzed by the Analysis Unit. The Proposal Unit proposes part-time jobs that match the user's work history and skills, for example, using a generative AI. The Proposal Unit helps users find jobs where they can make use of their experience. The Skill Proposal Unit proposes jobs that will help users improve their skills based on the part-time job information proposed by the Proposal Unit. The Skill Proposal Unit provides opportunities to improve the user's skills, for example, using a generative AI. The Skill Proposal Unit helps users find part-time jobs that will improve their skills. As a result, the part-time job matching system according to the embodiment makes it easier for users to search for part-time jobs and find jobs that match their work history and skills. Some or all of the above-described processes in the Reception Unit, Search Unit, Analysis Unit, Proposal Unit, and Skill Proposal Unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the Reception Unit provides an interface for receiving user input and can analyze the input content using a generative AI.The search unit uses generative AI to search for part-time job information and suggest the most suitable candidates. The analysis unit uses generative AI to analyze the user's work history and skills and find jobs that match. The suggestion unit uses generative AI to suggest the most suitable part-time jobs and helps users find jobs where they can utilize their experience. The skill suggestion unit uses generative AI to suggest jobs that will help users improve their skills and helps users find part-time jobs that will improve their skills.

[0030] The reception desk receives user input for part-time job searches. For example, the reception desk provides an interface for users to input keywords and conditions when searching for part-time jobs. Specifically, it provides a web page or mobile app interface with forms and checkboxes where users can input conditions such as desired work location, working hours, salary, and job type. The information entered by the user is saved to a database in real time and passed on to the next processing step. Furthermore, the reception desk has a function to analyze the user's input and automatically complete input errors or incomplete information. For example, if a user enters "restaurant," it will automatically suggest related keywords and categories to help the user enter more specific conditions. In addition, the reception desk can refer to the user's past search history and application history and automatically display conditions that the user has previously entered, saving the user the trouble of re-entering information. In this way, the reception desk can support users in efficiently searching for part-time jobs and improve the user experience.

[0031] The search unit searches for part-time job information based on the information received by the reception unit. For example, the search unit uses a generative AI to search for the most suitable part-time job information based on the user's input. Specifically, the generative AI analyzes the user's input using natural language processing technology and extracts relevant keywords and phrases. Next, the generative AI refers to the database of part-time job information and finds part-time jobs that match the user's conditions. For example, if the user enters "I'm looking for a part-time job at a restaurant," the generative AI searches the database for relevant part-time job information based on keywords such as "restaurant," "part-time job," "location," and "salary." Furthermore, the search unit can consider the user's past search history and application history and prioritize displaying part-time job information that the user has previously shown interest in. In this way, the search unit helps users efficiently find the most suitable part-time job information. The search unit also has a function to update search results in real time and immediately notify the user when new part-time job information is added. This allows users to always search for part-time jobs based on the latest information.

[0032] The analysis unit analyzes the user's work history and skills based on the part-time job information retrieved by the search unit. For example, the analysis unit uses generative AI to analyze the user's work history and skills and find suitable jobs. Specifically, the generative AI analyzes the work history and skill information entered by the user, and evaluates past experience, acquired qualifications, and skill sets in detail. For example, if a user enters "I have experience in customer service at a restaurant," the generative AI will extract relevant skills such as "customer service," "restaurant," and "communication skills," and suggest the most suitable part-time job based on these. The analysis unit can also suggest part-time jobs that will allow the user to acquire skills and experience that will be useful in the future, based on the user's work history and skills. In this way, the analysis unit helps users find part-time jobs that align with their career path. Furthermore, the analysis unit can also provide advice and recommended skills for future career advancement based on the user's work history and skills. In this way, the analysis unit can provide support for users to plan their careers systematically and support long-term career development.

[0033] The suggestion department proposes the most suitable part-time jobs based on information analyzed by the analysis department. For example, the suggestion department uses generative AI to suggest part-time jobs that match the user's work history and skills. Specifically, the generative AI lists the most suitable part-time jobs based on the user's work history and skill information and proposes them to the user. For example, if the user enters "I have experience in customer service at a restaurant," the generative AI will suggest part-time jobs such as customer service at a restaurant or barista at a cafe, based on related skills such as "customer service," "restaurant," and "communication skills." The suggestion department can also prioritize displaying part-time job information that the user is likely to be interested in, taking into account the user's desired conditions and past application history. In this way, the suggestion department helps users find jobs where they can utilize their experience. Furthermore, the suggestion department also has a function to provide feedback on the suggested part-time jobs. For example, by providing feedback such as "interested" or "not interested" to the suggested part-time jobs, the generative AI can learn the user's preferences and tendencies and improve the accuracy of future suggestions. In this way, the suggestion department can provide more personalized part-time job suggestions to users and improve the user experience.

[0034] The Skill Suggestion Department proposes jobs that will help users improve their skills based on part-time job information suggested by the department. For example, the Skill Suggestion Department uses generative AI to provide opportunities for users to improve their skills. Specifically, the generative AI compares the user's current skill set with the skills that will be needed in the future and identifies skill gaps. Next, the generative AI proposes part-time jobs to fill those skill gaps. For example, if a user inputs "I want to improve my customer service skills," the generative AI will propose part-time jobs that improve skills such as "customer service," "communication," and "customer support." The Skill Suggestion Department can also provide skill development opportunities aligned with the user's future career path. For example, if a user inputs "I want to become a manager in the future," the generative AI will propose part-time jobs that improve skills such as "leadership," "team management," and "project management." In this way, the Skill Suggestion Department helps users find part-time jobs that will improve their skills. Furthermore, the Skill Suggestion Department also has a function to provide feedback to users on the proposed part-time jobs. For example, by providing feedback to users on suggested part-time jobs, such as "helped me improve my skills" or "did not help me improve my skills," the generating AI can learn about the effectiveness of the user's skill development and improve the accuracy of future suggestions. This allows the skill suggestion department to provide users with more effective skill development opportunities and support their career development.

[0035] The reception desk can analyze the user's past job search history and select the optimal input method. For example, the reception desk can automatically display as suggestions the types of part-time jobs the user has frequently entered in the past. 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 the types of part-time jobs to be used during specific time periods based on the user's past input history. This improves user convenience by selecting the optimal input method based on past history. 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 past input history data into a generating AI and have the generating AI select the optimal input method.

[0036] The reception desk can filter the input content when a user is searching for a part-time job, based on their current living situation and areas of interest. For example, if the user is a student, the reception desk will prioritize displaying part-time jobs that do not interfere with their studies. If the user has a particular hobby, the reception desk can also prioritize displaying part-time jobs related to that hobby. If the user has a particular skill, the reception desk can also prioritize displaying part-time jobs that utilize that skill. In this way, by filtering the input content based on the user's living situation and areas of interest, more appropriate part-time job information can be provided. 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 living situation data into a generating AI and have the generating AI perform the filtering of the input content.

[0037] The reception desk can prioritize accepting relevant inputs when users are searching for part-time jobs, taking into account their geographical location. For example, the reception desk can prioritize displaying part-time jobs close to the user's current location. If the user is interested in a particular area, the reception desk can also prioritize displaying part-time jobs in that area. The reception desk can also prioritize displaying part-time jobs within a commutable distance for the user. This allows the reception desk to provide more relevant part-time job information by considering the user's geographical location. 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 geographical location information into a generating AI and have the generating AI select the most relevant inputs.

[0038] The reception desk can analyze a user's social media activity when they enter their job search information and accept relevant input. For example, the reception desk can prioritize displaying part-time jobs in areas the user has shown interest in on social media. The reception desk can also prioritize displaying part-time jobs from companies the user follows on social media. The reception desk can also prioritize displaying part-time jobs related to communities the user participates in on social media. This allows the reception desk to provide more relevant part-time job information by analyzing the user's 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 select relevant input content.

[0039] The search unit can adjust the level of detail in search results based on the importance of the part-time job information during a search. For example, the search unit can display highly important part-time job information in detail and simplify less important information. The search unit can also prioritize the display of highly important part-time job information and postpone the display of less important information. The search unit can also provide additional detailed information for highly important part-time job information. This allows for the prioritization of more important information by adjusting the level of detail in search results based on the importance of the part-time job information. Some or all of the above processing in the search unit may be performed using AI, for example, or without AI. For example, the search unit can input the importance data of the part-time job information into a generating AI and have the generating AI perform the adjustment of the level of detail in the search results.

[0040] The search unit can apply different search algorithms depending on the category of the part-time job information during a search. For example, the search unit can apply a specific search algorithm to part-time jobs in the food and beverage industry to display the best results. The search unit can also apply a different search algorithm to part-time jobs in the IT industry to display the best results. The search unit can also apply yet another search algorithm to part-time jobs in the customer service industry to display the best results. In this way, by applying different search algorithms depending on the category of the part-time job information, more optimal search results can be provided. Some or all of the above processing in the search unit may be performed using AI, for example, or without AI. For example, the search unit can input the category data of the part-time job information into a generating AI and have the generating AI execute the application of the search algorithm.

[0041] The search unit can determine the priority of search results based on when the part-time job information was updated. For example, the search unit can prioritize displaying the latest part-time job information and delay displaying older information. The search unit can also prioritize displaying part-time job information that is updated frequently. The search unit can also highlight and display the most recent information based on the update date. In this way, by determining the priority of search results based on when the part-time job information was updated, the latest information can be provided preferentially. Some or all of the above processing in the search unit may be performed using AI, for example, or without AI. For example, the search unit can input the update date data of the part-time job information into a generating AI and have the generating AI perform the determination of the search result priority.

[0042] The search unit can adjust the order of search results based on the relevance of the part-time job information during a search. For example, the search unit can prioritize displaying the part-time job information most relevant to the user's search query. The search unit can also display highly relevant information at the top and less relevant information at the bottom. The search unit can also highlight and display the most appropriate part-time job information based on its relevance. In this way, by adjusting the order of search results based on the relevance of the part-time job information, more relevant information can be provided. Some or all of the above processing in the search unit may be performed using AI, for example, or without AI. For example, the search unit can input the relevance data of the part-time job information into a generating AI and have the generating AI perform the adjustment of the order of search results.

[0043] The analysis unit can optimize its analysis algorithm by referring to the user's past work history and skill data during analysis. For example, the analysis unit can select the optimal analysis algorithm based on the user's past work history. The analysis unit can also customize the analysis algorithm by referring to the user's skill data. The analysis unit can also analyze the user's past work history and skill data and apply the most appropriate analysis algorithm. This allows for the provision of more optimal analysis results by referring to the user's past work history and skill data. 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 and skill data into a generating AI and have the generating AI perform the optimization of the analysis algorithm.

[0044] The analysis unit can apply different analysis methods depending on the user's work history and skill categories during analysis. For example, the analysis unit can apply a specific analysis method to provide optimal results for work history in the service industry. The analysis unit can also apply a different analysis method to provide optimal results for IT-related skills. The analysis unit can also apply yet another analysis method to provide optimal results for work history in the food and beverage industry. In this way, by applying different analysis methods depending on the user's work history and skill categories, more optimal analysis results can be provided. 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 work history and skill category data into a generating AI and have the generating AI execute the application of analysis methods.

[0045] The analysis unit can prioritize analysis results based on the submission timing of the user's work history and skills during the analysis process. For example, the analysis unit may prioritize the analysis of the most recent work history and skills data, and postpone the analysis of older data. The analysis unit can also highlight the most recent data based on the submission timing. The analysis unit can also prioritize the analysis of the most important data based on the submission timing. This allows for the provision of more up-to-date information by prioritizing analysis results based on the submission timing of the user's work history and skills. Some or all of the above processes in the analysis unit may be performed using AI, for example, or not. For example, the analysis unit can input the user's work history and skills submission timing data into a generating AI and have the generating AI perform the determination of the analysis result prioritization.

[0046] The analysis unit can adjust the order of analysis results based on the relevance of the user's work history and skills during analysis. For example, the analysis unit prioritizes analyzing the most relevant data based on the relevance of the user's work history and skills. The analysis unit can also display highly relevant data at the top and less relevant data at the bottom. The analysis unit can also highlight and display the most appropriate analysis results based on relevance. This allows for the provision of more relevant information by adjusting the order of analysis results based on the relevance of the user's work history and skills. 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 relevance data of the user's work history and skills into a generating AI and have the generating AI perform the adjustment of the order of analysis results.

[0047] The proposal unit can adjust the level of detail in its proposals based on the importance of the part-time jobs. For example, the proposal unit can propose highly important part-time jobs in detail and simplify less important ones. The proposal unit can also prioritize proposing highly important part-time jobs and postpone less important ones. The proposal unit can also provide additional details for highly important part-time jobs. This allows for the prioritization of more important information by adjusting the level of detail in proposals based on the importance of the part-time jobs. Some or all of the above processing in the proposal unit may be performed using AI, for example, or not using AI. For example, the proposal unit can input part-time job importance data into a generating AI and have the generating AI perform the adjustment of the level of detail in the proposals.

[0048] The suggestion unit can apply different suggestion algorithms depending on the category of the part-time job when making suggestions. For example, the suggestion unit can apply a specific suggestion algorithm to part-time job information in the food service industry to display the best results. The suggestion unit can also apply a different suggestion algorithm to part-time job information in the IT industry to display the best results. The suggestion unit can also apply yet another suggestion algorithm to part-time job information in the customer service industry to display the best results. In this way, by applying different suggestion algorithms depending on the category of the part-time job, it is possible to provide more optimal suggestions. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without using AI. For example, the suggestion unit can input part-time job category data into a generating AI and have the generating AI execute the application of the suggestion algorithm.

[0049] The proposal department can determine the priority of proposals based on the renewal dates of part-time jobs when making a proposal. For example, the proposal department can prioritize the most recent part-time job information and postpone older information. The proposal department can also prioritize part-time job information that is updated frequently. The proposal department can also highlight the most recent information based on the renewal date. This ensures that the latest information is provided preferentially by determining the priority of proposals based on the renewal dates of part-time jobs. Some or all of the above processing in the proposal department may be performed using AI, for example, or not using AI. For example, the proposal department can input part-time job renewal date data into a generating AI and have the generating AI perform the determination of proposal priorities.

[0050] The suggestion unit can adjust the order of suggestions based on the relevance of the part-time jobs when making suggestions. For example, the suggestion unit can prioritize suggesting the part-time job information most relevant to the user's search query. The suggestion unit can also display highly relevant information at the top and less relevant information at the bottom. The suggestion unit can also highlight and suggest the most appropriate part-time job information based on relevance. This allows for the provision of more relevant information by adjusting the order of suggestions based on the relevance of the part-time jobs. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or not using AI. For example, the suggestion unit can input the relevance data of the part-time jobs into a generating AI and have the generating AI perform the adjustment of the order of suggestions.

[0051] The skill suggestion unit can select the optimal skill-up method by referring to the user's past skill data when suggesting skills. For example, the skill suggestion unit can select the optimal skill-up method based on the user's past skill data. The skill suggestion unit can also customize the skill-up method by referring to the user's skill data. The skill suggestion unit can also analyze the user's past skill data and suggest the most appropriate skill-up method. This allows the skill suggestion unit to provide a more optimal skill-up method by referring to the user's past skill data. Some or all of the above processes in the skill suggestion unit may be performed using AI, for example, or without AI. For example, the skill suggestion unit can input the user's past skill data into a generating AI and have the generating AI select a skill-up method.

[0052] The skill suggestion unit can customize the means of skill improvement based on the user's current skill level when suggesting skills. For example, the skill suggestion unit can suggest the optimal means of skill improvement based on the user's current skill level. The skill suggestion unit can also customize the means of skill improvement according to the user's skill level. The skill suggestion unit can also analyze the user's current skill level and suggest the most appropriate means of skill improvement. By customizing the means of skill improvement based on the user's current skill level, it can provide more appropriate suggestions. Some or all of the above processing in the skill suggestion unit may be performed using AI, for example, or without AI. For example, the skill suggestion unit can input the user's current skill level data into a generating AI and have the generating AI perform the customization of the means of skill improvement.

[0053] The skill suggestion unit can select the most suitable skill-building method when suggesting skills, taking into account the user's geographical location. For example, the skill suggestion unit can prioritize suggesting skill-building methods close to the user's current location. If the user is interested in a particular region, the skill suggestion unit can also prioritize suggesting skill-building methods in that region. The skill suggestion unit can also prioritize suggesting skill-building methods within the user's commuting distance. By considering the user's geographical location, it is possible to provide more relevant skill-building methods. Some or all of the above processing in the skill suggestion unit may be performed using AI, for example, or without AI. For example, the skill suggestion unit can input the user's geographical location information into a generating AI and have the generating AI select skill-building methods.

[0054] The Skill Suggestion Department can analyze a user's social media activity and suggest ways to improve their skills when making skill suggestions. For example, the Skill Suggestion Department can prioritize suggesting skill improvement methods in areas the user has shown interest in on social media. The Skill Suggestion Department can also prioritize suggesting skill improvement methods related to companies the user follows on social media. The Skill Suggestion Department can also prioritize suggesting skill improvement methods related to communities the user participates in on social media. This allows the Skill Suggestion Department to provide more relevant skill improvement methods by analyzing the user's social media activity. Some or all of the above processing in the Skill Suggestion Department may be performed using AI, for example, or not using AI. For example, the Skill Suggestion Department can input the user's social media activity data into a generating AI and have the generating AI execute skill improvement method suggestions.

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

[0056] The part-time job matching system can also include a health monitoring unit that monitors the user's health status and adjusts part-time job suggestions based on that status. For example, if a user is feeling fatigued, it can prioritize suggesting light work. If the user is healthy, it can also suggest physically demanding part-time jobs. This allows for the suggestion of appropriate part-time jobs according to the user's health status. The health monitoring unit can collect user health data and analyze it using AI.

[0057] The part-time job matching system may include a rating analysis unit that collects users' past part-time job ratings and adjusts part-time job suggestions based on those ratings. For example, it can prioritize suggesting part-time jobs similar to those the user has given high ratings for in the past. It can also suggest jobs that the user has given low ratings for. This allows the system to suggest appropriate part-time jobs based on the user's past ratings. The rating analysis unit can collect user rating data and analyze it using generative AI.

[0058] The part-time job matching system can include a rhythm analysis unit that analyzes the user's lifestyle and adjusts part-time job suggestions based on that lifestyle. For example, if the user has a nocturnal lifestyle, the system can prioritize suggesting nighttime part-time jobs. If the user has an early-rising lifestyle, it can also suggest early-morning part-time jobs. This allows the system to suggest part-time jobs that are appropriate for the user's lifestyle. The rhythm analysis unit can collect the user's lifestyle data and analyze it using a generating AI.

[0059] The part-time job matching system can include a hobby analysis unit that analyzes the user's hobbies and interests and adjusts part-time job suggestions based on those interests. For example, if a user is interested in music, the system can prioritize suggesting music-related part-time jobs. If a user is interested in sports, it can also suggest sports-related part-time jobs. This allows the system to suggest appropriate part-time jobs that match the user's hobbies and interests. The hobby analysis unit can collect user hobby and interest data and analyze it using a generative AI.

[0060] The part-time job matching system can include a learning analysis unit that analyzes the user's learning history and adjusts part-time job suggestions based on that history. For example, if a user is studying a particular field, the system will prioritize suggesting part-time jobs related to that field. If a user wants to try a new field, the system can also suggest part-time jobs in that field. This allows the system to suggest appropriate part-time jobs according to the user's learning history. The learning analysis unit can collect the user's learning data and analyze it using generative AI.

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

[0062] Step 1: The reception desk receives user input regarding part-time job searches. For example, when a user searches for a part-time job, the reception desk provides an interface for entering keywords and conditions, receives the information entered by the user, and passes it on to the next process. Step 2: The search unit searches for part-time job information based on the information received by the reception unit. For example, it uses a generation AI to search for the most suitable part-time job information based on the user's input and refers to a database of part-time job information to find a part-time job that matches the user's criteria. Step 3: The analysis unit analyzes the user's work history and skills based on the part-time job information retrieved by the search unit. For example, it uses a generative AI to analyze the user's work history and skills and find a suitable job. Step 4: The proposal department proposes the most suitable part-time jobs based on the information analyzed by the analysis department. For example, it uses generative AI to suggest part-time jobs that match the user's work history and skills, helping the user find a job where they can make use of their experience. Step 5: The Skill Suggestion Department proposes jobs that will help users improve their skills based on the part-time job information suggested by the Suggestion Department. For example, it uses generative AI to provide opportunities for users to improve their skills and helps users find part-time jobs that will enhance their skills.

[0063] (Example of form 2) The part-time job matching system according to an embodiment of the present invention is a system that utilizes generative AI to streamline the search for part-time jobs and find jobs that match the user's work history and skills. When a user searches for a part-time job, the generative AI suggests the most suitable part-time job with a single search. Next, the generative AI analyzes the user's work history and skills and finds a job that matches them. Furthermore, the generative AI suggests jobs that will help the user improve their skills. For example, if a user enters "I'm looking for a part-time job at a restaurant," the generative AI searches for restaurant part-time job information all at once and suggests the best candidates. This allows the user to obtain a lot of part-time job information in a short time. Next, the generative AI analyzes the user's work history and skills and finds a job that matches them. For example, if a user has experience in the service industry, the generative AI takes that experience into consideration and prioritizes suggesting service industry part-time jobs. This allows the user to find a job that utilizes their experience. Furthermore, the generative AI suggests jobs that will help the user improve their skills. For example, if a user has programming skills, the generative AI suggests IT-related part-time jobs that can utilize those skills. This allows the user to have an opportunity to improve their skills. This system streamlines the part-time job search process, allowing users to find work that matches their work history and skills. Furthermore, by helping users find jobs that contribute to skill development, it also contributes to their career advancement. For example, students can gain practical experience through part-time work, which can be beneficial for their future job search. Thus, the part-time job matching system utilizing generational AI shortens the time spent searching for part-time jobs and helps users find work that matches their work history and skills, making it a beneficial system for users of all ages. In short, the part-time job matching system streamlines the part-time job search process and helps users find work that matches their work history and skills.

[0064] The part-time job matching system according to this embodiment comprises a reception unit, a search unit, an analysis unit, a suggestion unit, and a skill suggestion unit. The reception unit receives input from the user regarding their part-time job search. The reception unit provides, for example, an interface for the user to input keywords and conditions when searching for a part-time job. The reception unit receives the information entered by the user and passes it on to the next process. The search unit searches for part-time job information based on the information received by the reception unit. The search unit searches for the most suitable part-time job information based on the user's input, for example, using a generation AI. The search unit refers to a database of part-time job information and finds part-time jobs that match the user's conditions. For example, if the user enters "I'm looking for a part-time job at a restaurant," the search unit searches for restaurant part-time job information all at once and suggests the most suitable candidates. The analysis unit analyzes the user's work history and skills based on the part-time job information retrieved by the search unit. The analysis unit analyzes the user's work history and skills, for example, using a generation AI, and finds jobs that match them. The analysis unit considers the user's past work history and skills and provides information to suggest the most suitable part-time job. The Proposal Unit proposes the most suitable part-time jobs based on the information analyzed by the Analysis Unit. The Proposal Unit proposes part-time jobs that match the user's work history and skills, for example, using a generative AI. The Proposal Unit helps users find jobs where they can make use of their experience. The Skill Proposal Unit proposes jobs that will help users improve their skills based on the part-time job information proposed by the Proposal Unit. The Skill Proposal Unit provides opportunities to improve the user's skills, for example, using a generative AI. The Skill Proposal Unit helps users find part-time jobs that will improve their skills. As a result, the part-time job matching system according to the embodiment makes it easier for users to search for part-time jobs and find jobs that match their work history and skills. Some or all of the above-described processes in the Reception Unit, Search Unit, Analysis Unit, Proposal Unit, and Skill Proposal Unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the Reception Unit provides an interface for receiving user input and can analyze the input content using a generative AI.The search unit uses generative AI to search for part-time job information and suggest the most suitable candidates. The analysis unit uses generative AI to analyze the user's work history and skills and find jobs that match. The suggestion unit uses generative AI to suggest the most suitable part-time jobs and helps users find jobs where they can utilize their experience. The skill suggestion unit uses generative AI to suggest jobs that will help users improve their skills and helps users find part-time jobs that will improve their skills.

[0065] The reception desk receives user input for part-time job searches. For example, the reception desk provides an interface for users to input keywords and conditions when searching for part-time jobs. Specifically, it provides a web page or mobile app interface with forms and checkboxes where users can input conditions such as desired work location, working hours, salary, and job type. The information entered by the user is saved to a database in real time and passed on to the next processing step. Furthermore, the reception desk has a function to analyze the user's input and automatically complete input errors or incomplete information. For example, if a user enters "restaurant," it will automatically suggest related keywords and categories to help the user enter more specific conditions. In addition, the reception desk can refer to the user's past search history and application history and automatically display conditions that the user has previously entered, saving the user the trouble of re-entering information. In this way, the reception desk can support users in efficiently searching for part-time jobs and improve the user experience.

[0066] The search unit searches for part-time job information based on the information received by the reception unit. For example, the search unit uses a generative AI to search for the most suitable part-time job information based on the user's input. Specifically, the generative AI analyzes the user's input using natural language processing technology and extracts relevant keywords and phrases. Next, the generative AI refers to the database of part-time job information and finds part-time jobs that match the user's conditions. For example, if the user enters "I'm looking for a part-time job at a restaurant," the generative AI searches the database for relevant part-time job information based on keywords such as "restaurant," "part-time job," "location," and "salary." Furthermore, the search unit can consider the user's past search history and application history and prioritize displaying part-time job information that the user has previously shown interest in. In this way, the search unit helps users efficiently find the most suitable part-time job information. The search unit also has a function to update search results in real time and immediately notify the user when new part-time job information is added. This allows users to always search for part-time jobs based on the latest information.

[0067] The analysis unit analyzes the user's work history and skills based on the part-time job information retrieved by the search unit. For example, the analysis unit uses generative AI to analyze the user's work history and skills and find suitable jobs. Specifically, the generative AI analyzes the work history and skill information entered by the user, and evaluates past experience, acquired qualifications, and skill sets in detail. For example, if a user enters "I have experience in customer service at a restaurant," the generative AI will extract relevant skills such as "customer service," "restaurant," and "communication skills," and suggest the most suitable part-time job based on these. The analysis unit can also suggest part-time jobs that will allow the user to acquire skills and experience that will be useful in the future, based on the user's work history and skills. In this way, the analysis unit helps users find part-time jobs that align with their career path. Furthermore, the analysis unit can also provide advice and recommended skills for future career advancement based on the user's work history and skills. In this way, the analysis unit can provide support for users to plan their careers systematically and support long-term career development.

[0068] The suggestion department proposes the most suitable part-time jobs based on information analyzed by the analysis department. For example, the suggestion department uses generative AI to suggest part-time jobs that match the user's work history and skills. Specifically, the generative AI lists the most suitable part-time jobs based on the user's work history and skill information and proposes them to the user. For example, if the user enters "I have experience in customer service at a restaurant," the generative AI will suggest part-time jobs such as customer service at a restaurant or barista at a cafe, based on related skills such as "customer service," "restaurant," and "communication skills." The suggestion department can also prioritize displaying part-time job information that the user is likely to be interested in, taking into account the user's desired conditions and past application history. In this way, the suggestion department helps users find jobs where they can utilize their experience. Furthermore, the suggestion department also has a function to provide feedback on the suggested part-time jobs. For example, by providing feedback such as "interested" or "not interested" to the suggested part-time jobs, the generative AI can learn the user's preferences and tendencies and improve the accuracy of future suggestions. In this way, the suggestion department can provide more personalized part-time job suggestions to users and improve the user experience.

[0069] The Skill Suggestion Department proposes jobs that will help users improve their skills based on part-time job information suggested by the department. For example, the Skill Suggestion Department uses generative AI to provide opportunities for users to improve their skills. Specifically, the generative AI compares the user's current skill set with the skills that will be needed in the future and identifies skill gaps. Next, the generative AI proposes part-time jobs to fill those skill gaps. For example, if a user inputs "I want to improve my customer service skills," the generative AI will propose part-time jobs that improve skills such as "customer service," "communication," and "customer support." The Skill Suggestion Department can also provide skill development opportunities aligned with the user's future career path. For example, if a user inputs "I want to become a manager in the future," the generative AI will propose part-time jobs that improve skills such as "leadership," "team management," and "project management." In this way, the Skill Suggestion Department helps users find part-time jobs that will improve their skills. Furthermore, the Skill Suggestion Department also has a function to provide feedback to users on the proposed part-time jobs. For example, by providing feedback to users on suggested part-time jobs, such as "helped me improve my skills" or "did not help me improve my skills," the generating AI can learn about the effectiveness of the user's skill development and improve the accuracy of future suggestions. This allows the skill suggestion department to provide users with more effective skill development opportunities and support their career development.

[0070] The reception desk can estimate the user's emotions and adjust the input method for job searching based on the estimated emotions. For example, if the user is stressed, the reception desk can provide a simple interface and minimize the input steps. If the user is relaxed, the reception desk can also provide detailed input options and suggest a customizable input method. If the user is in a hurry, the reception desk can prioritize voice input to allow for quick job search input. This allows for more appropriate job searching by adjusting the input method 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 reception desk may be performed using AI or not using AI. 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.

[0071] The reception desk can analyze the user's past job search history and select the optimal input method. For example, the reception desk can automatically display as suggestions the types of part-time jobs the user has frequently entered in the past. 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 the types of part-time jobs to be used during specific time periods based on the user's past input history. This improves user convenience by selecting the optimal input method based on past history. 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 past input history data into a generating AI and have the generating AI select the optimal input method.

[0072] The reception desk can filter the input content when a user is searching for a part-time job, based on their current living situation and areas of interest. For example, if the user is a student, the reception desk will prioritize displaying part-time jobs that do not interfere with their studies. If the user has a particular hobby, the reception desk can also prioritize displaying part-time jobs related to that hobby. If the user has a particular skill, the reception desk can also prioritize displaying part-time jobs that utilize that skill. In this way, by filtering the input content based on the user's living situation and areas of interest, more appropriate part-time job information can be provided. 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 living situation data into a generating AI and have the generating AI perform the filtering of the input content.

[0073] The reception desk can estimate the user's emotions and prioritize input items based on the estimated emotions. For example, if the user is stressed, the reception desk may prioritize displaying simple input items. If the user is relaxed, the reception desk may also prioritize displaying detailed input items. If the user is in a hurry, the reception desk may also prioritize displaying the most important input items. This allows for more efficient input by prioritizing input items 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.

[0074] The reception desk can prioritize accepting relevant inputs when users are searching for part-time jobs, taking into account their geographical location. For example, the reception desk can prioritize displaying part-time jobs close to the user's current location. If the user is interested in a particular area, the reception desk can also prioritize displaying part-time jobs in that area. The reception desk can also prioritize displaying part-time jobs within a commutable distance for the user. This allows the reception desk to provide more relevant part-time job information by considering the user's geographical location. 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 geographical location information into a generating AI and have the generating AI select the most relevant inputs.

[0075] The reception desk can analyze a user's social media activity when they enter their job search information and accept relevant input. For example, the reception desk can prioritize displaying part-time jobs in areas the user has shown interest in on social media. The reception desk can also prioritize displaying part-time jobs from companies the user follows on social media. The reception desk can also prioritize displaying part-time jobs related to communities the user participates in on social media. This allows the reception desk to provide more relevant part-time job information by analyzing the user's 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 select relevant input content.

[0076] The search unit can estimate the user's emotions and adjust how search results are displayed based on the estimated emotions. For example, if the user is stressed, the search unit can display simple and highly visible search results. If the user is relaxed, the search unit can also display search results containing detailed information. If the user is in a hurry, the search unit can also display search results that highlight the most important information. This allows for more appropriate search results to be provided by adjusting how search results are displayed 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 search unit may be performed using AI, or not using AI. For example, the search unit can input user facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0077] The search unit can adjust the level of detail in search results based on the importance of the part-time job information during a search. For example, the search unit can display highly important part-time job information in detail and simplify less important information. The search unit can also prioritize the display of highly important part-time job information and postpone the display of less important information. The search unit can also provide additional detailed information for highly important part-time job information. This allows for the prioritization of more important information by adjusting the level of detail in search results based on the importance of the part-time job information. Some or all of the above processing in the search unit may be performed using AI, for example, or without AI. For example, the search unit can input the importance data of the part-time job information into a generating AI and have the generating AI perform the adjustment of the level of detail in the search results.

[0078] The search unit can apply different search algorithms depending on the category of the part-time job information during a search. For example, the search unit can apply a specific search algorithm to part-time jobs in the food and beverage industry to display the best results. The search unit can also apply a different search algorithm to part-time jobs in the IT industry to display the best results. The search unit can also apply yet another search algorithm to part-time jobs in the customer service industry to display the best results. In this way, by applying different search algorithms depending on the category of the part-time job information, more optimal search results can be provided. Some or all of the above processing in the search unit may be performed using AI, for example, or without AI. For example, the search unit can input the category data of the part-time job information into a generating AI and have the generating AI execute the application of the search algorithm.

[0079] The search unit can estimate the user's emotions and adjust the length of search results based on the estimated emotions. For example, if the user is stressed, the search unit can display short, concise search results. If the user is relaxed, the search unit can also display longer search results containing more detailed information. If the user is in a hurry, the search unit can display short search results highlighting the most important information. By adjusting the length of search results according to the user's emotions, more appropriate search results can be provided. 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 search unit may be performed using AI, or not using AI. For example, the search unit can input user facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0080] The search unit can determine the priority of search results based on when the part-time job information was updated. For example, the search unit can prioritize displaying the latest part-time job information and delay displaying older information. The search unit can also prioritize displaying part-time job information that is updated frequently. The search unit can also highlight and display the most recent information based on the update date. In this way, by determining the priority of search results based on when the part-time job information was updated, the latest information can be provided preferentially. Some or all of the above processing in the search unit may be performed using AI, for example, or without AI. For example, the search unit can input the update date data of the part-time job information into a generating AI and have the generating AI perform the determination of the search result priority.

[0081] The search unit can adjust the order of search results based on the relevance of the part-time job information during a search. For example, the search unit can prioritize displaying the part-time job information most relevant to the user's search query. The search unit can also display highly relevant information at the top and less relevant information at the bottom. The search unit can also highlight and display the most appropriate part-time job information based on its relevance. In this way, by adjusting the order of search results based on the relevance of the part-time job information, more relevant information can be provided. Some or all of the above processing in the search unit may be performed using AI, for example, or without AI. For example, the search unit can input the relevance data of the part-time job information into a generating AI and have the generating AI perform the adjustment of the order of search results.

[0082] The analysis unit can estimate the user's emotions and adjust the analysis method for work history and skills based on the estimated user emotions. For example, if the user is stressed, the analysis unit provides simple and easy-to-understand analysis results. If the user is relaxed, the analysis unit can also provide analysis results that include detailed information. If the user is in a hurry, the analysis unit can also provide analysis results that highlight the most important information. By adjusting the analysis method according to the user's emotions, more appropriate analysis results can be provided. 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.

[0083] The analysis unit can optimize its analysis algorithm by referring to the user's past work history and skill data during analysis. For example, the analysis unit can select the optimal analysis algorithm based on the user's past work history. The analysis unit can also customize the analysis algorithm by referring to the user's skill data. The analysis unit can also analyze the user's past work history and skill data and apply the most appropriate analysis algorithm. This allows for the provision of more optimal analysis results by referring to the user's past work history and skill data. 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 and skill data into a generating AI and have the generating AI perform the optimization of the analysis algorithm.

[0084] The analysis unit can apply different analysis methods depending on the user's work history and skill categories during analysis. For example, the analysis unit can apply a specific analysis method to provide optimal results for work history in the service industry. The analysis unit can also apply a different analysis method to provide optimal results for IT-related skills. The analysis unit can also apply yet another analysis method to provide optimal results for work history in the food and beverage industry. In this way, by applying different analysis methods depending on the user's work history and skill categories, more optimal analysis results can be provided. 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 work history and skill category data into a generating AI and have the generating AI execute the application of analysis methods.

[0085] The analysis unit can estimate the user's emotions and adjust how the analysis results are displayed based on the estimated emotions. For example, if the user is stressed, the analysis unit can display simple and easy-to-read analysis results. If the user is relaxed, the analysis unit can also display analysis results that include detailed information. If the user is in a hurry, the analysis unit can also display analysis results that highlight the most important information. This allows for more appropriate analysis results to be provided by adjusting how the analysis results are displayed 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-described processes 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.

[0086] The analysis unit can prioritize analysis results based on the submission timing of the user's work history and skills during the analysis process. For example, the analysis unit may prioritize the analysis of the most recent work history and skills data, and postpone the analysis of older data. The analysis unit can also highlight the most recent data based on the submission timing. The analysis unit can also prioritize the analysis of the most important data based on the submission timing. This allows for the provision of more up-to-date information by prioritizing analysis results based on the submission timing of the user's work history and skills. Some or all of the above processes in the analysis unit may be performed using AI, for example, or not. For example, the analysis unit can input the user's work history and skills submission timing data into a generating AI and have the generating AI perform the determination of the analysis result prioritization.

[0087] The analysis unit can adjust the order of analysis results based on the relevance of the user's work history and skills during analysis. For example, the analysis unit prioritizes analyzing the most relevant data based on the relevance of the user's work history and skills. The analysis unit can also display highly relevant data at the top and less relevant data at the bottom. The analysis unit can also highlight and display the most appropriate analysis results based on relevance. This allows for the provision of more relevant information by adjusting the order of analysis results based on the relevance of the user's work history and skills. 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 relevance data of the user's work history and skills into a generating AI and have the generating AI perform the adjustment of the order of analysis results.

[0088] The suggestion unit can estimate the user's emotions and adjust the way it presents suggestions based on those emotions. For example, if the user is stressed, the suggestion unit can present simple and highly visible suggestions. If the user is relaxed, the suggestion unit can also present suggestions that include detailed information. If the user is in a hurry, the suggestion unit can present suggestions that highlight the most important information. By adjusting the way suggestions are presented according to the user's emotions, more appropriate suggestions can be provided. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the processing described above in the suggestion unit may be performed using AI or not. For example, the suggestion unit can input user facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0089] The proposal unit can adjust the level of detail in its proposals based on the importance of the part-time jobs. For example, the proposal unit can propose highly important part-time jobs in detail and simplify less important ones. The proposal unit can also prioritize proposing highly important part-time jobs and postpone less important ones. The proposal unit can also provide additional details for highly important part-time jobs. This allows for the prioritization of more important information by adjusting the level of detail in proposals based on the importance of the part-time jobs. Some or all of the above processing in the proposal unit may be performed using AI, for example, or not using AI. For example, the proposal unit can input part-time job importance data into a generating AI and have the generating AI perform the adjustment of the level of detail in the proposals.

[0090] The suggestion unit can apply different suggestion algorithms depending on the category of the part-time job when making suggestions. For example, the suggestion unit can apply a specific suggestion algorithm to part-time job information in the food service industry to display the best results. The suggestion unit can also apply a different suggestion algorithm to part-time job information in the IT industry to display the best results. The suggestion unit can also apply yet another suggestion algorithm to part-time job information in the customer service industry to display the best results. In this way, by applying different suggestion algorithms depending on the category of the part-time job, it is possible to provide more optimal suggestions. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without using AI. For example, the suggestion unit can input part-time job category data into a generating AI and have the generating AI execute the application of the suggestion algorithm.

[0091] The suggestion unit can estimate the user's emotions and adjust the length of the suggestions based on the estimated emotions. For example, if the user is stressed, the suggestion unit can provide short, concise suggestions. If the user is relaxed, the suggestion unit can provide longer suggestions with more detailed information. If the user is in a hurry, the suggestion unit can provide short suggestions that highlight the most important information. By adjusting the length of suggestions according to the user's emotions, more appropriate suggestions can be provided. 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 processing described above in the suggestion unit may be performed using AI or not using AI. For example, the suggestion unit can input user facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0092] The proposal department can determine the priority of proposals based on the renewal dates of part-time jobs when making a proposal. For example, the proposal department can prioritize the most recent part-time job information and postpone older information. The proposal department can also prioritize part-time job information that is updated frequently. The proposal department can also highlight the most recent information based on the renewal date. This ensures that the latest information is provided preferentially by determining the priority of proposals based on the renewal dates of part-time jobs. Some or all of the above processing in the proposal department may be performed using AI, for example, or not using AI. For example, the proposal department can input part-time job renewal date data into a generating AI and have the generating AI perform the determination of proposal priorities.

[0093] The suggestion unit can adjust the order of suggestions based on the relevance of the part-time jobs when making suggestions. For example, the suggestion unit can prioritize suggesting the part-time job information most relevant to the user's search query. The suggestion unit can also display highly relevant information at the top and less relevant information at the bottom. The suggestion unit can also highlight and suggest the most appropriate part-time job information based on relevance. This allows for the provision of more relevant information by adjusting the order of suggestions based on the relevance of the part-time jobs. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or not using AI. For example, the suggestion unit can input the relevance data of the part-time jobs into a generating AI and have the generating AI perform the adjustment of the order of suggestions.

[0094] The skill suggestion unit can estimate the user's emotions and adjust its skill improvement suggestions based on those emotions. For example, if the user is stressed, the skill suggestion unit can provide simple and highly visible skill improvement suggestions. If the user is relaxed, the skill suggestion unit can also provide skill improvement suggestions that include detailed information. If the user is in a hurry, the skill suggestion unit can also provide skill improvement suggestions that highlight the most important information. By adjusting the skill improvement suggestion method according to the user's emotions, more appropriate suggestions can be provided. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the skill suggestion unit may be performed using AI or not using AI. For example, the skill suggestion unit can input user facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0095] The skill suggestion unit can select the optimal skill-up method by referring to the user's past skill data when suggesting skills. For example, the skill suggestion unit can select the optimal skill-up method based on the user's past skill data. The skill suggestion unit can also customize the skill-up method by referring to the user's skill data. The skill suggestion unit can also analyze the user's past skill data and suggest the most appropriate skill-up method. This allows the skill suggestion unit to provide a more optimal skill-up method by referring to the user's past skill data. Some or all of the above processes in the skill suggestion unit may be performed using AI, for example, or without AI. For example, the skill suggestion unit can input the user's past skill data into a generating AI and have the generating AI select a skill-up method.

[0096] The skill suggestion unit can customize the means of skill improvement based on the user's current skill level when suggesting skills. For example, the skill suggestion unit can suggest the optimal means of skill improvement based on the user's current skill level. The skill suggestion unit can also customize the means of skill improvement according to the user's skill level. The skill suggestion unit can also analyze the user's current skill level and suggest the most appropriate means of skill improvement. By customizing the means of skill improvement based on the user's current skill level, it can provide more appropriate suggestions. Some or all of the above processing in the skill suggestion unit may be performed using AI, for example, or without AI. For example, the skill suggestion unit can input the user's current skill level data into a generating AI and have the generating AI perform the customization of the means of skill improvement.

[0097] The skill suggestion unit can estimate the user's emotions and determine the priority of skill development based on the estimated emotions. For example, if the user is stressed, the skill suggestion unit will prioritize suggesting simple skill development methods. If the user is relaxed, the skill suggestion unit may also prioritize suggesting detailed skill development methods. If the user is in a hurry, the skill suggestion unit may also prioritize suggesting the most important skill development methods. This allows for more appropriate suggestions by determining the priority of skill development 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 skill suggestion unit may be performed using AI or not using AI. For example, the skill suggestion unit can input user facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0098] The skill suggestion unit can select the most suitable skill-building method when suggesting skills, taking into account the user's geographical location. For example, the skill suggestion unit can prioritize suggesting skill-building methods close to the user's current location. If the user is interested in a particular region, the skill suggestion unit can also prioritize suggesting skill-building methods in that region. The skill suggestion unit can also prioritize suggesting skill-building methods within the user's commuting distance. By considering the user's geographical location, it is possible to provide more relevant skill-building methods. Some or all of the above processing in the skill suggestion unit may be performed using AI, for example, or without AI. For example, the skill suggestion unit can input the user's geographical location information into a generating AI and have the generating AI select skill-building methods.

[0099] The Skill Suggestion Department can analyze a user's social media activity and suggest ways to improve their skills when making skill suggestions. For example, the Skill Suggestion Department can prioritize suggesting skill improvement methods in areas the user has shown interest in on social media. The Skill Suggestion Department can also prioritize suggesting skill improvement methods related to companies the user follows on social media. The Skill Suggestion Department can also prioritize suggesting skill improvement methods related to communities the user participates in on social media. This allows the Skill Suggestion Department to provide more relevant skill improvement methods by analyzing the user's social media activity. Some or all of the above processing in the Skill Suggestion Department may be performed using AI, for example, or not using AI. For example, the Skill Suggestion Department can input the user's social media activity data into a generating AI and have the generating AI execute skill improvement method suggestions.

[0100] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0101] The part-time job matching system can also include a health monitoring unit that monitors the user's health status and adjusts part-time job suggestions based on that status. For example, if a user is feeling fatigued, it can prioritize suggesting light work. If the user is healthy, it can also suggest physically demanding part-time jobs. This allows for the suggestion of appropriate part-time jobs according to the user's health status. The health monitoring unit can collect user health data and analyze it using AI.

[0102] The part-time job matching system can include an emotion adjustment unit that estimates the user's emotions and adjusts job suggestions based on those emotions. For example, if the user is feeling stressed, it can suggest part-time jobs in a relaxing environment. If the user is excited, it can also suggest challenging part-time jobs. This allows the system to suggest appropriate part-time jobs that match the user's emotions. The emotion adjustment unit can collect user emotion data and analyze it using generative AI.

[0103] The part-time job matching system may include a rating analysis unit that collects users' past part-time job ratings and adjusts part-time job suggestions based on those ratings. For example, it can prioritize suggesting part-time jobs similar to those the user has given high ratings for in the past. It can also suggest jobs that the user has given low ratings for. This allows the system to suggest appropriate part-time jobs based on the user's past ratings. The rating analysis unit can collect user rating data and analyze it using generative AI.

[0104] The part-time job matching system can include an emotional feedback unit that estimates the user's emotions and adjusts job suggestions based on those emotions. For example, if the user is feeling anxious, it can suggest part-time jobs with a support system. If the user is confident, it can suggest part-time jobs where they can demonstrate leadership. This allows the system to suggest appropriate part-time jobs that match the user's emotions. The emotional feedback unit can collect user emotion data and analyze it using generative AI.

[0105] The part-time job matching system can include a rhythm analysis unit that analyzes the user's lifestyle and adjusts part-time job suggestions based on that lifestyle. For example, if the user has a nocturnal lifestyle, the system can prioritize suggesting nighttime part-time jobs. If the user has an early-rising lifestyle, it can also suggest early-morning part-time jobs. This allows the system to suggest part-time jobs that are appropriate for the user's lifestyle. The rhythm analysis unit can collect the user's lifestyle data and analyze it using a generating AI.

[0106] The part-time job matching system can include an emotion adaptation unit that estimates the user's emotions and adjusts job suggestions based on those emotions. For example, if the user is feeling down, it might suggest part-time jobs that will cheer them up. If the user is feeling energetic, it might suggest part-time jobs that require active work. This allows the system to suggest appropriate part-time jobs that match the user's emotions. The emotion adaptation unit can collect user emotion data and analyze it using generative AI.

[0107] The part-time job matching system can include a hobby analysis unit that analyzes the user's hobbies and interests and adjusts part-time job suggestions based on those interests. For example, if a user is interested in music, the system can prioritize suggesting music-related part-time jobs. If a user is interested in sports, it can also suggest sports-related part-time jobs. This allows the system to suggest appropriate part-time jobs that match the user's hobbies and interests. The hobby analysis unit can collect user hobby and interest data and analyze it using a generative AI.

[0108] The part-time job matching system can include an emotion balancing unit that estimates the user's emotions and adjusts job suggestions based on those emotions. For example, if the user is feeling stressed, it can suggest part-time jobs in a relaxing environment. If the user is relaxed, it can also suggest challenging part-time jobs. This allows the system to suggest appropriate part-time jobs that match the user's emotions. The emotion balancing unit can collect user emotion data and analyze it using generative AI.

[0109] The part-time job matching system can include a learning analysis unit that analyzes the user's learning history and adjusts part-time job suggestions based on that history. For example, if a user is studying a particular field, the system will prioritize suggesting part-time jobs related to that field. If a user wants to try a new field, the system can also suggest part-time jobs in that field. This allows the system to suggest appropriate part-time jobs according to the user's learning history. The learning analysis unit can collect the user's learning data and analyze it using generative AI.

[0110] The part-time job matching system can include an emotion insight unit that estimates the user's emotions and adjusts part-time job suggestions based on those estimated emotions. For example, if the user is feeling happy, it can suggest part-time jobs that will make them feel even happier. If the user is feeling sad, it can also suggest part-time jobs that will lift their spirits. This allows the system to suggest part-time jobs that are appropriate to the user's emotions. The emotion insight unit can collect user emotion data and analyze it using generative AI.

[0111] The following briefly describes the processing flow for example form 2.

[0112] Step 1: The reception desk receives user input regarding part-time job searches. For example, when a user searches for a part-time job, the reception desk provides an interface for entering keywords and conditions, receives the information entered by the user, and passes it on to the next process. Step 2: The search unit searches for part-time job information based on the information received by the reception unit. For example, it uses a generation AI to search for the most suitable part-time job information based on the user's input and refers to a database of part-time job information to find a part-time job that matches the user's criteria. Step 3: The analysis unit analyzes the user's work history and skills based on the part-time job information retrieved by the search unit. For example, it uses a generative AI to analyze the user's work history and skills and find a suitable job. Step 4: The proposal department proposes the most suitable part-time jobs based on the information analyzed by the analysis department. For example, it uses generative AI to suggest part-time jobs that match the user's work history and skills, helping the user find a job where they can make use of their experience. Step 5: The Skill Suggestion Department proposes jobs that will help users improve their skills based on the part-time job information suggested by the Suggestion Department. For example, it uses generative AI to provide opportunities for users to improve their skills and helps users find part-time jobs that will enhance their skills.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] Each of the multiple elements described above, including the reception unit, search unit, analysis unit, proposal unit, and skill proposal 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 reception device 38 of the smart device 14 and receives input from the user looking for part-time work. The search unit is implemented by the specific processing unit 290 of the data processing unit 12 and searches for optimal part-time work information using generating AI. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the user's work history and skills. The proposal unit is implemented by the specific processing unit 290 of the data processing unit 12 and proposes part-time work that matches the user's work history and skills. The skill proposal unit is implemented by the specific processing unit 290 of the data processing unit 12 and proposes work that will help the user improve their skills. 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.

[0117] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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).

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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.).

[0129] 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.

[0130] 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.

[0131] 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.

[0132] Each of the multiple elements described above, including the reception unit, search unit, analysis unit, proposal unit, and skill proposal unit, is implemented, for example, by at least one of the smart glasses 214 and the data processing unit 12. For example, the reception unit is implemented by the microphone 238 of the smart glasses 214 and receives input from the user looking for part-time work. The search unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and searches for optimal part-time work information using generating AI. The analysis unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and analyzes the user's work history and skills. The proposal unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and proposes part-time work that matches the user's work history and skills. The skill proposal unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and proposes work that will help the user improve their skills. 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.

[0133] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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).

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.).

[0145] 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.

[0146] 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.

[0147] 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.

[0148] Each of the multiple elements described above, including the reception unit, search unit, analysis unit, proposal unit, and skill proposal unit, is implemented by, for example, at least one of the headset terminal 314 and the data processing unit 12. For example, the reception unit is implemented by the microphone 238 of the headset terminal 314 and receives input from the user looking for part-time work. The search unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and searches for optimal part-time work information using generating AI. The analysis unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and analyzes the user's work history and skills. The proposal unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and proposes part-time work that matches the user's work history and skills. The skill proposal unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and proposes work that will help the user improve their skills. 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.

[0149] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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).

[0155] 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.

[0156] 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.

[0157] 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.

[0158] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0159] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0160] In 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.

[0161] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0162] The specific processing unit 290 transmits the result of the specific processing to the 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.

[0163] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0164] The data processing system 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.

[0165] Each of the multiple elements described above, including the reception unit, search unit, analysis unit, proposal unit, and skill proposal 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 microphone 238 of the robot 414 and receives input from the user looking for a part-time job. The search unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and searches for the most suitable part-time job information using generating AI. The analysis unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and analyzes the user's work history and skills. The proposal unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and proposes part-time jobs that match the user's work history and skills. The skill proposal unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and proposes jobs that will help the user improve their skills. 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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."

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] (Note 1) A reception desk that accepts user inputs for part-time job searches, A search unit that searches for part-time job information based on the information received by the reception unit, An analysis unit analyzes the user's work history and skills based on the part-time job information retrieved by the aforementioned search unit, Based on the information analyzed by the aforementioned analysis unit, a proposal unit proposes the most suitable part-time job, The system includes a skill suggestion unit that proposes jobs that will help the user improve their skills based on the part-time job information proposed by the aforementioned suggestion unit. A system characterized by the following features. (Note 2) The aforementioned reception unit is The system estimates the user's emotions and adjusts the input method for part-time job searches based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned reception unit is Analyze the user's past job search history and select the optimal input method. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned reception unit is When users enter their job search information, the system filters their input based on their current living situation 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 prioritizes input content based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned reception unit is When users enter information for part-time job searches, the system prioritizes accepting input that is highly relevant, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned reception unit is When users enter information for a part-time job search, the system analyzes their social media activity and accepts relevant input. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned search unit, It estimates the user's sentiment and adjusts how search results are displayed based on that estimated sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned search unit, When searching, adjust the level of detail in search results based on the importance of the part-time job information. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned search unit, When searching, different search algorithms are applied depending on the category of the part-time job information. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned search unit, It estimates the user's sentiment and adjusts the length of search results based on the estimated sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned search unit, When searching, search results are prioritized based on when the part-time job information was last updated. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned search unit, When searching, the order of search results is adjusted based on the relevance of the part-time job information. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit, We estimate the user's emotions and adjust the analysis method of work history and skills based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit, During analysis, the analysis algorithm is optimized by referencing the user's past work history and skill data. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit, During analysis, different analysis methods are applied depending on the user's work history and skill categories. The system described in Appendix 1, characterized by the features described herein. (Note 17) 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 18) The aforementioned analysis unit, During analysis, the priority of analysis results is determined based on when the user submitted their work history and skills. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned analysis unit, During analysis, the order of the analysis results is adjusted based on the relevance of the user's work history and skills. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned proposal section is, It estimates the user's emotions and adjusts the way suggestions are presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned proposal section is, When making a proposal, adjust the level of detail in the proposal based on the importance of the part-time work. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned proposal section is, When making a proposal, different proposal algorithms are applied depending on the category of the part-time job. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned proposal section is, It estimates the user's emotions and adjusts the length of the suggestion based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned proposal section is, When submitting proposals, prioritize them based on the contract renewal dates for part-time employees. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned proposal section is, When making proposals, adjust the order of proposals based on the relevance of the part-time work. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned skill proposal unit, It estimates the user's emotions and adjusts the method of suggesting skill improvements based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned skill proposal unit, When suggesting skills, the system selects the most suitable skill development method by referring to the user's past skill data. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned skill proposal unit, When suggesting skills, customize the means of skill development based on the user's current skill level. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned skill proposal unit, It estimates the user's emotions and determines the priority of skill development based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned skill proposal unit, When proposing skills, the system selects the most suitable skill development method by considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned skill proposal unit, When suggesting skills, we analyze the user's social media activity and propose ways to improve those skills. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

[0185] 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 user inputs for part-time job searches, A search unit that searches for part-time job information based on the information received by the reception unit, An analysis unit analyzes the user's work history and skills based on the part-time job information retrieved by the aforementioned search unit, Based on the information analyzed by the aforementioned analysis unit, a proposal unit proposes the most suitable part-time job, The system includes a skill suggestion unit that proposes jobs that will help the user improve their skills based on the part-time job information proposed by the aforementioned suggestion unit. A system characterized by the following features.

2. The aforementioned reception unit is The system estimates the user's emotions and adjusts the input method for part-time job searches based on those estimated emotions. The system according to feature 1.

3. The aforementioned reception unit is Analyze the user's past job search history and select the optimal input method. The system according to feature 1.

4. The aforementioned reception unit is When users enter their job search information, the system filters their input based on their current living situation and areas of interest. The system according to feature 1.

5. The aforementioned reception unit is It estimates the user's emotions and prioritizes input content based on those estimated emotions. The system according to feature 1.

6. The aforementioned reception unit is When users enter information for part-time job searches, the system prioritizes accepting input that is highly relevant, taking into account the user's geographical location. The system according to feature 1.

7. The aforementioned reception unit is When users enter information for a part-time job search, the system analyzes their social media activity and accepts relevant input. The system according to feature 1.

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

9. The aforementioned search unit, When searching, adjust the level of detail in search results based on the importance of the part-time job information. The system according to feature 1.

Images