system

Abstract

The system according to the embodiment aims to efficiently match companies with workers. [Solution] A system according to an embodiment includes a collection unit, an analysis unit, a determination unit, and a notification unit. The collection unit collects information on companies and workers. The analysis unit analyzes the information collected by the collection unit. The determination unit calculates a matching degree based on the information analyzed by the analysis unit. The notification unit issues a notification based on the matching degree calculated by the determination unit.

Description

[Technical Field]

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

[0002] Patent document 1 discloses a persona chatbot control method 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 a description of the chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] Japanese Patent Publication No. 2022-180282 Summary of the Invention [Problem to be solved by the invention]

[0004] Conventional technology does not efficiently match companies with workers, and there is room for improvement.

[0005] The system according to the embodiment aims to efficiently match companies with workers. [Means for solving the problem]

[0006] The system according to the embodiment includes a collection unit, an analysis unit, a determination unit, and a notification unit. The collection unit collects information on companies and workers. The analysis unit analyzes the information collected by the collection unit. The determination unit calculates a matching degree based on the information analyzed by the analysis unit. The notification unit issues a notification based on the matching degree calculated by the determination unit. [Effects of the Invention]

[0007] The system according to the embodiment can efficiently match companies with workers. [Brief explanation of the drawings]

[0008] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. DETAILED DESCRIPTION OF THE INVENTION

[0009] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

[0010] First, the terms used in the following description will be explained.

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

[0012] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

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

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

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

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

[0017] 1, a 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, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

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

[0020] The reception device 38 includes a touch panel 38A and a microphone 38B, and receives user input. The touch panel 38A detects contact with a pointer (for example, a pen or a finger) to receive user input by the touch of the pointer. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 (see FIG. 2) acquires the data indicating the user input.

[0021] Output device 40 includes a display 40A and a speaker 40B, and presents data to a user by outputting the data in a form of expression that the user can perceive (e.g., audio and / or text). Display 40A displays visible information such as text and images in accordance with instructions from processor 46. Speaker 40B outputs audio in accordance with instructions from processor 46. Camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

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

[0024] 2, in the data processing device 12, a specific process 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" according to the technology of the present 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 process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0025] The storage 32 stores a data generation model 58 and an 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0026] In the smart device 14, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used together 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 the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart device 14 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0027] Note that a device other than the data processing device 12 may 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 a processing result (prediction result, etc.) using the data generation model 58 by communicating with the server device having the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device owned by a user (e.g., a mobile phone, a robot, a home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example 1) A matching system according to an embodiment of the present invention uses AI to match companies and workers. In this matching system, companies input requirements such as the skills, experience, and work location they are seeking, while workers input their own information, such as their skills, experience, and desired work location. AI analyzes this information and determines the degree of match between the company and the worker. For example, a company inputs requirements such as specific programming language skills, project management experience, and a work location in Tokyo, while a worker inputs information such as programming language skills, past project experience, and a desired work location in Tokyo. This information is input into AI, which analyzes the company and worker information to determine the degree of match. For example, it determines whether the skills the company is seeking match the worker's skills, whether the experience the company is seeking match the worker's experience, and whether the work location matches. This calculates the degree of match between the company and the worker. If the degree of match is high, a notification is sent to the company and the worker. For example, the company may receive a notification stating, "This worker meets your requirements," and the worker may receive a notification stating, "This company matches your skills and preferences." This allows the company and the worker to conduct interviews and detailed meetings. This system allows companies to efficiently find suitable talent, and workers to find jobs that match their skills and aspirations. For example, companies can quickly find workers who match their desired skills and experience, making the recruitment process more efficient. In addition, workers can find jobs that match their skills and aspirations, improving their satisfaction. This allows the matching system to efficiently collect, analyze, assess, and notify information about companies and workers.

[0029] The matching system according to the embodiment includes a collection unit, an analysis unit, a determination unit, and a notification unit. The collection unit collects information about companies and workers. The information about companies and workers includes, for example, resume information, company recruitment information, and skill sets, but is not limited to these examples. The collection unit collects information from the Internet using, for example, web scraping technology. The collection unit can also automatically obtain company recruitment information using API integration. The collection unit can also collect information directly from companies and workers by manual input. The analysis unit analyzes the information collected by the collection unit. The analysis is performed using, for example, data mining technology, but is not limited to these examples. For example, the analysis unit analyzes company recruitment information and worker resume information using text analysis technology. The analysis unit can also evaluate the degree of match between the company and worker skill sets using statistical analysis technology. The determination unit calculates the degree of match based on the information analyzed by the analysis unit. The degree of match is calculated using, for example, a scoring algorithm, but is not limited to these examples. For example, the determination unit scores the degree of match between the skills required by the company and the skills of the worker. The determination unit can also evaluate the degree of match between the experience required by the company and the worker's experience based on a weighting criterion. The notification unit provides notification based on the degree of match calculated by the determination unit. Notification can be provided, for example, by email notification or push notification, but is not limited to these examples. For example, the notification unit can send an email notification to the company stating, "This worker meets your requirements." The notification unit can also send a push notification to the worker stating, "This company meets your skills and preferences." This allows the matching system according to the embodiment to efficiently collect, analyze, determine, and notify information about companies and workers.

[0030] The matching system further includes a soft skills analysis unit that analyzes soft skills. The soft skills analysis unit analyzes soft skills. Soft skills include, but are not limited to, communication skills, leadership skills, and problem-solving skills. The soft skills analysis unit, for example, analyzes the worker's resume and self-promotional statement using text analysis technology. The soft skills analysis unit can also evaluate the worker's soft skills through analysis of survey results. The soft skills analysis unit can also analyze the worker's soft skills based on interview evaluations. For example, the soft skills analysis unit can analyze the interviewer's evaluation comments and evaluate the worker's communication skills. The soft skills analysis unit can also statistically analyze the survey results and evaluate the worker's leadership skills. This enables more appropriate matching by taking soft skills into consideration. Some or all of the above-described processing in the soft skills analysis unit may be performed using, for example, AI, or may be performed without AI. For example, the soft skills analysis unit can input the worker's resume data into a generation AI and have the generation AI perform a soft skills analysis.

[0031] The matching system further includes a feedback unit that provides detailed feedback on the degree of matching. The feedback unit provides detailed feedback on the degree of matching. Detailed feedback includes, but is not limited to, a breakdown of the score, suggestions for improvement, and examples of success stories. For example, the feedback unit displays a breakdown of the score and provides a detailed explanation of each element of the degree of match between the company and the worker. The feedback unit can also suggest improvements to companies and workers. For example, the feedback unit may suggest to companies, "Broadening the skill set you are seeking will increase your chances of matching with more candidates." The feedback unit may also suggest to workers, "Improving specific skills will increase your chances of matching with more companies." Furthermore, the feedback unit can introduce success stories to suggest specific ways to improve to companies and workers. For example, the feedback unit may provide specific advice to companies and workers based on past successful matching cases. Providing detailed feedback improves user satisfaction. Some or all of the above-described processing by the feedback unit may be performed using, for example, AI, or may be performed without AI. For example, the feedback unit can input score data of the matching degree to the generation AI and cause the generation AI to generate detailed feedback.

[0032] The collection unit can analyze a company's past hiring history and select an appropriate information collection method. For example, the collection unit selects a similar information collection method based on a hiring method that was successful in the past. For example, the collection unit retrieves a company's past hiring history from a database and analyzes it using data mining technology. The collection unit can also optimize an information collection method for a specific skill set based on the past hiring history. For example, the collection unit compares the skill sets of previously hired workers with the company's job information and selects the optimal information collection method. The collection unit can also analyze the past hiring history and select the most effective information collection channel. For example, the collection unit selects similar channels based on information collection channels that were effective in the past (e.g., job sites, social media, etc.). In this way, the optimal information collection method can be selected by analyzing the past hiring history. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input a company's past hiring history data into a generation AI and have the generation AI select the optimal information collection method.

[0033] When collecting information, the collection unit can filter the information based on the company's current project or business situation. For example, the collection unit prioritizes collecting information about workers with skills related to ongoing projects. For example, the collection unit acquires ongoing project information from the company's project management system and filters information about workers with related skill sets. The collection unit can also filter information about workers with required skill sets according to the business situation. For example, the collection unit acquires business situation data from the company's business management system and filters information about workers with required skill sets. The collection unit can also collect information about workers with specific experience based on the company's business situation. For example, the collection unit analyzes the company's business situation data and prioritizes collecting information about workers with specific project experience. This allows more relevant information to be collected by filtering information based on the company's current project or business situation. Some or all of the above-mentioned processing by the collection unit may be performed using, for example, AI, or may be performed without AI. For example, the collection unit can input the company's business situation data into the generation AI and cause the generation AI to perform the filtering process.

[0034] When collecting information, the collection unit can prioritize collecting highly relevant information by taking into account the geographic location information of the company. The collection unit, for example, prioritizes collecting information about workers close to the company's location. For example, the collection unit obtains company location information from a geographic information system (GIS) and filters information about nearby workers. The collection unit can also filter and collect geographically relevant information. For example, the collection unit prioritizes collecting information about workers in a specific area based on the company's location. The collection unit can also collect information about workers in a specific area based on the company's geographic location. For example, the collection unit analyzes company location information and prioritizes collecting information about workers in a specific area. In this way, highly relevant information can be prioritized by taking into account the company's geographic location information. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input company location information into the generation AI and cause the generation AI to filter highly relevant information.

[0035] When collecting information, the collection unit can analyze the company's social media activities and collect related information. For example, the collection unit can collect information related to a specific skill set from the company's social media activities. For example, the collection unit can obtain posts from the company's social media accounts and extract related information using text analysis technology. The collection unit can also analyze the company's activities on social media and collect information on related workers. For example, the collection unit can analyze the company's social media activity data and collect information on workers with a specific skill set. The collection unit can also collect information on workers with specific experience based on the company's social media activities. For example, the collection unit can analyze the company's social media activity data and collect information on workers with specific project experience. In this way, related information can be collected by analyzing the company's social media activities. Some or all of the above-mentioned processing in the collection unit can be performed using, for example, AI, or can be performed without using AI. For example, the collection unit can input the company's social media activity data into the generation AI and cause the generation AI to collect related information.

[0036] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the information. For example, the analysis unit performs a detailed analysis on information with high importance. For example, the analysis unit evaluates the importance of a company's job information and performs a detailed analysis on the information with high importance. The analysis unit can also perform a simplified analysis on information with low importance. For example, the analysis unit evaluates the importance of worker resume information and performs a simplified analysis on the information with low importance. The analysis unit can also dynamically adjust the level of detail of the analysis according to the importance of the information. For example, the analysis unit evaluates the importance of a company's job information and worker resume information and adjusts the level of detail of the analysis according to the importance. This enables more appropriate analysis by adjusting the level of detail of the analysis based on the importance of the information. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the company's job information and the worker's resume information into a generation AI and have the generation AI adjust the level of detail of the analysis.

[0037] The analysis unit can apply different analysis algorithms depending on the category of information during analysis. For example, the analysis unit applies a specific analysis algorithm to skill information. For example, the analysis unit analyzes the worker's skill information using a clustering algorithm. The analysis unit can also apply different analysis algorithms to experience information. For example, the analysis unit analyzes the worker's experience information using regression analysis. The analysis unit can also apply a dedicated analysis algorithm to the desired work location information. For example, the analysis unit analyzes the worker's desired work location information using a classification algorithm. This enables more appropriate analysis by applying different analysis algorithms depending on the category of information. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the worker's skill information, experience information, and desired work location information into the generation AI and cause the generation AI to apply different analysis algorithms.

[0038] During analysis, the analysis unit can determine the priority of analysis based on the time of information submission. The analysis unit, for example, prioritizes the analysis of the most recent information. For example, the analysis unit evaluates the time of submission of a company's job information and prioritizes the analysis of the most recent information. The analysis unit can also perform a simplified analysis on older information. For example, the analysis unit evaluates the time of submission of a worker's resume information and performs a simplified analysis on older information. The analysis unit can also dynamically adjust the priority of analysis based on the time of submission. For example, the analysis unit evaluates the time of submission of a company's job information and a worker's resume information and adjusts the priority of analysis based on the time of submission. This enables more appropriate analysis by determining the priority of analysis based on the time of submission of the information. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without AI. For example, the analysis unit can input the company's job information and the worker's resume information into the generation AI and have the generation AI determine the priority of analysis.

[0039] During analysis, the analysis unit can adjust the order of analysis based on the relevance of the information. For example, the analysis unit prioritizes analysis of highly relevant information. For example, the analysis unit evaluates the relevance between a company's job information and the worker's resume information, and prioritizes analysis of the highly relevant information. The analysis unit can also perform a simplified analysis on less relevant information. For example, the analysis unit evaluates the relevance between a company's job information and the worker's resume information, and performs a simplified analysis on the less relevant information. The analysis unit can also dynamically adjust the order of analysis based on the relevance of the information. For example, the analysis unit evaluates the relevance between a company's job information and the worker's resume information, and adjusts the order of analysis based on the relevance. This enables more appropriate analysis by adjusting the order of analysis based on the relevance of the information. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the company's job information and the worker's resume information into the generation AI and cause the generation AI to adjust the order of analysis.

[0040] The determination unit can improve the accuracy of the determination by taking into account the interrelationship between information when making a determination. The determination unit makes a determination by taking into account, for example, the interrelationship between skills and experience. For example, the determination unit evaluates the interrelationship between skills and experience in a company's job information and the worker's resume information, and makes a determination. The determination unit can also make a determination by taking into account the interrelationship between a desired work location and skills. For example, the determination unit evaluates the interrelationship between a desired work location and skills in a company's job information and the worker's resume information, and makes a determination. The determination unit can also improve the accuracy of the determination based on the interrelationship between information. For example, the determination unit evaluates the interrelationship between a company's job information and the worker's resume information, and improves the accuracy of the determination. In this way, the accuracy of the determination is improved by taking into account the interrelationship between information. Some or all of the above-described processing in the determination unit may be performed using, for example, AI, or may be performed without using AI. For example, the determination unit can input the company's job information and the worker's resume information into a generation AI, and cause the generation AI to evaluate the interrelationship between the information.

[0041] When making a judgment, the judgment unit can make a judgment taking into account attribute information of the person submitting the information. The judgment unit makes a judgment taking into account, for example, the age of the person submitting the information. For example, the judgment unit evaluates the age of the person submitting the company's job information and the worker's resume information and makes a judgment. The judgment unit can also make a judgment taking into account the gender of the person submitting the information. For example, the judgment unit evaluates the gender of the person submitting the company's job information and the worker's resume information and makes a judgment. The judgment unit can also make a judgment taking into account the work history of the person submitting the information. For example, the judgment unit evaluates the work history of the person submitting the company's job information and the worker's resume information and makes a judgment. This enables a more appropriate judgment by taking into account the attribute information of the person submitting the information. Some or all of the above-mentioned processing in the judgment unit may be performed using, for example, AI, or may be performed without using AI. For example, the judgment unit can input the company's job information and the worker's resume information into a generation AI and cause the generation AI to evaluate the attribute information of the person submitting the information.

[0042] The determination unit can make a determination taking into account the geographical distribution of information. For example, the determination unit prioritizes geographically close information. For example, the determination unit evaluates the geographical distribution of company job information and worker resume information, and prioritizes geographically close information. The determination unit can also make a determination taking into account information that is highly geographically related. For example, the determination unit evaluates the geographical distribution of company job information and worker resume information, and makes a determination taking into account the highly related information. The determination unit can also improve the accuracy of the determination based on the geographical distribution. For example, the determination unit evaluates the geographical distribution of company job information and worker resume information, and improves the accuracy of the determination. This enables a more appropriate determination by taking the geographical distribution of the information into account. Some or all of the above-described processing in the determination unit may be performed using, for example, AI, or may be performed without using AI. For example, the determination unit can input company job information and worker resume information into a generation AI and cause the generation AI to evaluate the geographical distribution.

[0043] The determination unit can improve the accuracy of the determination by referring to related literature for the information when making the determination. The determination unit, for example, determines the compatibility of skills based on related literature. For example, the determination unit evaluates the compatibility of skills between a company's job information and the worker's resume information, and makes the determination by referring to related literature. The determination unit can also determine the compatibility of experience based on related literature. For example, the determination unit evaluates the compatibility of experience between a company's job information and the worker's resume information, and makes the determination by referring to related literature. The determination unit can also improve the accuracy of the determination by referring to related literature. For example, the determination unit evaluates the compatibility of related literature for a company's job information and the worker's resume information, and improves the accuracy of the determination. As a result, the accuracy of the determination is improved by referring to related literature for the information. Some or all of the above-described processing in the determination unit may be performed using, for example, AI, or may be performed without using AI. For example, the determination unit can input the company's job information and the worker's resume information into the generation AI and cause the generation AI to refer to related literature.

[0044] The notification unit can select the optimal notification method by referring to past notification history when making a notification. For example, the notification unit may perform notification using a similar method based on a notification method that was effective in the past. For example, the notification unit may retrieve past notification history from a database and analyze it using data mining technology. The notification unit can also optimize a specific notification method from the past notification history. For example, the notification unit may perform notification using a similar method based on a notification method that was effective in the past. The notification unit can also analyze past notification history and select the most effective notification channel. For example, the notification unit may select a similar channel based on a notification channel that was effective in the past (e.g., email notification, push notification, etc.). In this way, the optimal notification method can be selected by referring to the past notification history. Some or all of the above-described processing in the notification unit may be performed using, or without, AI. For example, the notification unit may input past notification history data into a generation AI and have the generation AI select the optimal notification method.

[0045] The notification unit can determine the priority of notifications based on the importance of the information when sending notifications. For example, the notification unit prioritizes notification of information with high importance. For example, the notification unit evaluates the importance of a company's job information and prioritizes notification of information with high importance. The notification unit can also provide a simplified notification for information with low importance. For example, the notification unit evaluates the importance of worker resume information and provides a simplified notification for information with low importance. The notification unit can also dynamically adjust the priority of notifications according to the importance of the information. For example, the notification unit evaluates the importance of a company's job information and worker resume information and adjusts the priority of notifications according to the importance. This enables more appropriate notifications by determining the priority of notifications based on the importance of the information. Some or all of the above-described processing in the notification unit may be performed using, for example, AI, or may be performed without using AI. For example, the notification unit can input the company's job information and worker resume information into a generation AI and have the generation AI determine the priority of notifications.

[0046] The notification unit can select the optimal notification method by taking into account the user's device information when providing a notification. For example, if the user is using a smartphone, the notification unit provides a push notification. For example, the notification unit acquires the user's device information and provides a push notification when the user is using a smartphone. The notification unit can also provide a notification optimized for a large screen when the user is using a tablet. For example, the notification unit acquires the user's device information and provides a notification optimized for a large screen when the user is using a tablet. The notification unit can also provide a concise and highly visible notification when the user is using a smartwatch. For example, the notification unit acquires the user's device information and provides a concise and highly visible notification when the user is using a smartwatch. This allows the optimal notification method to be selected by taking into account the user's device information. Some or all of the above-described processing in the notification unit may be performed using, for example, AI, or may be performed without using AI. For example, the notification unit can input the user's device information into the generation AI and cause the generation AI to select the optimal notification method.

[0047] The notification unit may analyze the user's social media activity and provide relevant notifications at the time of notification. The notification unit may, for example, notify the user of relevant job information based on the user's social media activity. For example, the notification unit may acquire posts from the user's social media account and extract relevant job information using text analysis technology. The notification unit may also analyze the user's activity on social media and provide relevant company information. For example, the notification unit may analyze the user's social media activity data and provide relevant company information. The notification unit may also provide notifications related to a specific skill set based on the user's social media activity. For example, the notification unit may analyze the user's social media activity data and provide notifications related to a specific skill set. In this way, relevant notifications can be provided by analyzing the user's social media activity. Some or all of the above-described processing in the notification unit may be performed using, for example, AI, or may be performed without using AI. For example, the notification unit may input the user's social media activity data into a generation AI and cause the generation AI to generate relevant notifications.

[0048] The soft skills analysis unit can optimize the analysis algorithm by referring to past soft skill data during soft skill analysis. The soft skills analysis unit, for example, selects an optimal analysis algorithm based on past soft skill data. For example, the soft skills analysis unit retrieves past soft skill data from a database and analyzes it using data mining technology. The soft skills analysis unit can also analyze past soft skill data and optimize the analysis algorithm for a specific skill set. For example, the soft skills analysis unit selects an optimal analysis algorithm based on soft skill data evaluated in the past. The soft skills analysis unit can also improve the accuracy of the analysis by referring to past soft skill data. For example, the soft skills analysis unit analyzes past soft skill data and improves the accuracy of the analysis algorithm. In this way, the analysis algorithm can be optimized by referring to past soft skill data. Some or all of the above-described processing in the soft skills analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the soft skills analysis unit can input past soft skill data into a generation AI and cause the generation AI to optimize the analysis algorithm.

[0049] The soft skill analysis unit can perform soft skill analysis while taking into account the user's geographic location information. The soft skill analysis unit, for example, analyzes relevant soft skills based on the user's location. For example, the soft skill analysis unit obtains the user's geographic location information from a geographic information system (GIS) and analyzes relevant soft skills. The soft skill analysis unit can also prioritize analysis of soft skills with high geographic relevance. For example, the soft skill analysis unit prioritizes analysis of soft skills in a specific region based on the user's geographic location information. The soft skill analysis unit can also analyze soft skills in a specific region based on the user's geographic location information. For example, the soft skill analysis unit analyzes the user's geographic location information and prioritizes analysis of soft skills in a specific region. This enables more appropriate soft skill analysis by taking the user's geographic location information into consideration. Some or all of the above-described processing in the soft skill analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the soft skill analysis unit can input the user's geographic location information to a generation AI and cause the generation AI to perform a soft skill analysis.

[0050] When providing feedback, the feedback unit can select an optimal feedback method by referring to past feedback history. The feedback unit, for example, provides feedback using a similar method based on feedback methods that were effective in the past. For example, the feedback unit retrieves past feedback history from a database and analyzes it using data mining technology. The feedback unit can also optimize a specific feedback method from the past feedback history. For example, the feedback unit provides feedback using a similar method based on feedback methods that were effective in the past. The feedback unit can also analyze the past feedback history and select the most effective feedback channel. For example, the feedback unit selects a similar channel based on feedback channels that were effective in the past (e.g., email notification, push notification, etc.). In this way, the optimal feedback method can be selected by referring to the past feedback history. Some or all of the above-described processing in the feedback unit may be performed using, or without, AI. For example, the feedback unit can input past feedback history data to a generation AI and cause the generation AI to select an optimal feedback method.

[0051] When providing feedback, the feedback unit can select the optimal feedback method by taking into account the user's device information. For example, if the user is using a smartphone, the feedback unit provides feedback via push notification. For example, the feedback unit acquires the user's device information and provides feedback via push notification when the user is using a smartphone. Furthermore, if the user is using a tablet, the feedback unit can provide feedback optimized for a large screen. For example, the feedback unit acquires the user's device information and provides feedback optimized for a large screen when the user is using a tablet. Furthermore, the feedback unit can provide concise and highly visible feedback when the user is using a smartwatch. For example, the feedback unit acquires the user's device information and provides concise and highly visible feedback when the user is using a smartwatch. This allows the optimal feedback method to be selected by taking into account the user's device information. Some or all of the above-described processing in the feedback unit may be performed using, for example, AI, or may be performed without using AI. For example, the feedback unit can input the user's device information to the generation AI and cause the generation AI to select the optimal feedback method.

[0052] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0053] The collection unit can analyze a company's past hiring history and select an appropriate information collection method. For example, the collection unit selects a similar information collection method based on a hiring method that was successful in the past. For example, the collection unit retrieves a company's past hiring history from a database and analyzes it using data mining technology. The collection unit can also optimize an information collection method for a specific skill set based on the past hiring history. For example, the collection unit compares the skill sets of previously hired workers with the company's job information and selects the optimal information collection method. The collection unit can also analyze the past hiring history and select the most effective information collection channel. For example, the collection unit selects similar channels based on information collection channels that were effective in the past (e.g., job sites, social media, etc.). In this way, the optimal information collection method can be selected by analyzing the past hiring history. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input a company's past hiring history data into the generation AI and have the generation AI select the optimal information collection method.

[0054] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the information. For example, detailed analysis is performed on information with high importance. For example, the analysis unit evaluates the importance of a company's job information and performs a detailed analysis on the information with high importance. The analysis unit can also perform a simplified analysis on information with low importance. For example, the analysis unit evaluates the importance of worker resume information and performs a simplified analysis on the information with low importance. The analysis unit can also dynamically adjust the level of detail of the analysis according to the importance of the information. For example, the analysis unit evaluates the importance of a company's job information and worker resume information and adjusts the level of detail of the analysis according to the importance. This enables more appropriate analysis by adjusting the level of detail of the analysis based on the importance of the information. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the company's job information and the worker's resume information into a generation AI and have the generation AI adjust the level of detail of the analysis.

[0055] The judgment unit can improve the accuracy of the judgment by taking into account the interrelationship between information when making a judgment. For example, the judgment is made by taking into account the interrelationship between skills and experience. For example, the judgment unit evaluates the interrelationship between skills and experience in the company's job information and the worker's resume information and makes a judgment. The judgment unit can also make a judgment by taking into account the interrelationship between desired work location and skills. For example, the judgment unit evaluates the interrelationship between desired work location and skills in the company's job information and the worker's resume information and makes a judgment. The judgment unit can also improve the accuracy of the judgment based on the interrelationship between information. For example, the judgment unit evaluates the interrelationship between the company's job information and the worker's resume information and improves the accuracy of the judgment. In this way, the accuracy of the judgment is improved by taking the interrelationship between information into account. Some or all of the above-mentioned processing in the judgment unit may be performed using, for example, AI, or may be performed without using AI. For example, the judgment unit can input the company's job information and the worker's resume information into a generation AI and cause the generation AI to evaluate the interrelationship between the information.

[0056] When sending a notification, the notification unit can select the optimal notification method by referring to past notification history. For example, the notification unit can send a notification using a similar method based on a notification method that was effective in the past. For example, the notification unit can retrieve past notification history from a database and analyze it using data mining technology. The notification unit can also optimize a specific notification method from the past notification history. For example, the notification unit can send a notification using a similar method based on a notification method that was effective in the past. The notification unit can also analyze past notification history and select the most effective notification channel. For example, the notification unit can select a similar channel based on a notification channel that was effective in the past (e.g., email notification, push notification, etc.). In this way, the optimal notification method can be selected by referring to the past notification history. Some or all of the above-mentioned processing in the notification unit may be performed using, for example, AI, or may be performed without using AI. For example, the notification unit can input past notification history data to a generation AI and have the generation AI select the optimal notification method.

[0057] When providing feedback, the feedback unit can select the optimal feedback method by taking into account the user's device information. For example, if the user is using a smartphone, the feedback unit provides feedback via push notification. For example, the feedback unit acquires the user's device information and provides feedback via push notification when the user is using a smartphone. Furthermore, if the user is using a tablet, the feedback unit can provide feedback optimized for a large screen. For example, the feedback unit acquires the user's device information and provides feedback optimized for a large screen when the user is using a tablet. Furthermore, the feedback unit can provide concise and highly visible feedback when the user is using a smartwatch. For example, the feedback unit acquires the user's device information and provides concise and highly visible feedback when the user is using a smartwatch. This allows the optimal feedback method to be selected by taking into account the user's device information. Some or all of the above-described processing in the feedback unit may be performed using, for example, AI, or may be performed without using AI. For example, the feedback unit can input the user's device information into the generation AI and cause the generation AI to select the optimal feedback method.

[0058] The processing flow of the first embodiment will be briefly explained below.

[0059] Step 1: The collection unit collects information about companies and workers. This information includes, for example, resume information, company job information, and skill sets. The collection unit uses web scraping technology to collect information from the Internet. It can also automatically obtain company job information using API integration. It can also collect information directly from companies and workers by manually entering it. Step 2: The analysis unit analyzes the information collected by the collection unit. This analysis is carried out using data mining and text analysis techniques. For example, the analysis may analyze a company's job postings and the employee's resume, and use statistical analysis techniques to evaluate the degree of match between the company's and the employee's skill sets. Step 3: The determination unit calculates the degree of matching based on the information analyzed by the analysis unit. The degree of matching is calculated using a scoring algorithm. For example, the degree of match between the skills required by the company and the skills of the worker is scored, and the degree of match between the experience required by the company and the experience of the worker is evaluated based on weighting criteria. Step 4: The notification unit issues a notification based on the matching degree calculated by the determination unit. Notifications are sent via email or push notification. For example, an email notification stating "This worker meets your requirements" is sent to the company, and a push notification stating "This company matches your skills and preferences" is sent to the worker.

[0060] (Example 2) A matching system according to an embodiment of the present invention uses AI to match companies and workers. In this matching system, companies input requirements such as the skills, experience, and work location they are seeking, while workers input their own information, such as their skills, experience, and desired work location. AI analyzes this information and determines the degree of match between the company and the worker. For example, a company inputs requirements such as specific programming language skills, project management experience, and a work location in Tokyo, while a worker inputs information such as programming language skills, past project experience, and a desired work location in Tokyo. This information is input into AI, which analyzes the company and worker information to determine the degree of match. For example, it determines whether the skills the company is seeking match the worker's skills, whether the experience the company is seeking match the worker's experience, and whether the work location matches. This calculates the degree of match between the company and the worker. If the degree of match is high, a notification is sent to the company and the worker. For example, the company may receive a notification stating, "This worker meets your requirements," and the worker may receive a notification stating, "This company matches your skills and preferences." This allows the company and the worker to conduct interviews and detailed meetings. This system allows companies to efficiently find suitable talent, and workers to find jobs that match their skills and aspirations. For example, companies can quickly find workers who match their desired skills and experience, making the recruitment process more efficient. In addition, workers can find jobs that match their skills and aspirations, improving their satisfaction. This allows the matching system to efficiently collect, analyze, assess, and notify information about companies and workers.

[0061] The matching system according to the embodiment includes a collection unit, an analysis unit, a determination unit, and a notification unit. The collection unit collects information about companies and workers. The information about companies and workers includes, for example, resume information, company recruitment information, and skill sets, but is not limited to these examples. The collection unit collects information from the Internet using, for example, web scraping technology. The collection unit can also automatically obtain company recruitment information using API integration. The collection unit can also collect information directly from companies and workers by manual input. The analysis unit analyzes the information collected by the collection unit. The analysis is performed using, for example, data mining technology, but is not limited to these examples. For example, the analysis unit analyzes company recruitment information and worker resume information using text analysis technology. The analysis unit can also evaluate the degree of match between the company and worker skill sets using statistical analysis technology. The determination unit calculates the degree of match based on the information analyzed by the analysis unit. The degree of match is calculated using, for example, a scoring algorithm, but is not limited to these examples. For example, the determination unit scores the degree of match between the skills required by the company and the skills of the worker. The determination unit can also evaluate the degree of match between the experience required by the company and the worker's experience based on a weighting criterion. The notification unit provides notification based on the degree of match calculated by the determination unit. Notification can be provided, for example, by email notification or push notification, but is not limited to these examples. For example, the notification unit can send an email notification to the company stating, "This worker meets your requirements." The notification unit can also send a push notification to the worker stating, "This company meets your skills and preferences." This allows the matching system according to the embodiment to efficiently collect, analyze, determine, and notify information about companies and workers.

[0062] The matching system further includes a soft skills analysis unit that analyzes soft skills. The soft skills analysis unit analyzes soft skills. Soft skills include, but are not limited to, communication skills, leadership skills, and problem-solving skills. The soft skills analysis unit, for example, analyzes the worker's resume and self-promotional statement using text analysis technology. The soft skills analysis unit can also evaluate the worker's soft skills through analysis of survey results. The soft skills analysis unit can also analyze the worker's soft skills based on interview evaluations. For example, the soft skills analysis unit can analyze the interviewer's evaluation comments and evaluate the worker's communication skills. The soft skills analysis unit can also statistically analyze the survey results and evaluate the worker's leadership skills. This enables more appropriate matching by taking soft skills into consideration. Some or all of the above-described processing in the soft skills analysis unit may be performed using, for example, AI, or may be performed without AI. For example, the soft skills analysis unit can input the worker's resume data into a generation AI and have the generation AI perform a soft skills analysis.

[0063] The matching system further includes a feedback unit that provides detailed feedback on the degree of matching. The feedback unit provides detailed feedback on the degree of matching. Examples of detailed feedback include, but are not limited to, a breakdown of the score, suggestions for improvement, and examples of successful cases. For example, the feedback unit displays a breakdown of the score and provides a detailed explanation of each element of the degree of matching between the company and the worker. The feedback unit can also suggest improvements to companies and workers. For example, the feedback unit may suggest to companies, "By broadening the skill set you are seeking, you will increase your chances of matching with more candidates." The feedback unit may also suggest to workers, "By improving specific skills, you will increase your chances of matching with more companies." Furthermore, the feedback unit can introduce successful cases to suggest specific ways to improve to companies and workers. For example, the feedback unit may provide specific advice to companies and workers based on past successful matching cases. This detailed feedback improves user satisfaction. Some or all of the above-described processing by the feedback unit may be performed using, for example, AI, or may be performed without AI. For example, the feedback unit can input score data of the matching degree to the generation AI and cause the generation AI to generate detailed feedback.

[0064] The collection unit can estimate the user's emotions and adjust the timing of information collection based on the estimated user emotions. For example, if the user is feeling stressed, the collection unit collects information during a time when the user is able to relax. For example, the collection unit captures the user's facial expressions with a camera and estimates the user's emotions using an emotion estimation algorithm. Furthermore, if the user is relaxed, the collection unit can immediately start collecting information. For example, the collection unit records the user's voice and estimates the user's emotions using voice analysis technology. Furthermore, if the user is in a hurry, the collection unit can quickly collect information. For example, the collection unit collects the user's biometric data (heart rate and electrodermal activity) using a sensor and estimates the user's emotions using an emotion estimation algorithm. This allows for more appropriate information collection by adjusting the timing of information collection according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit may input image data of a user taken with a camera to the generation AI and cause the generation AI to estimate the user's emotions.

[0065] The collection unit can analyze a company's past hiring history and select an appropriate information collection method. For example, the collection unit selects a similar information collection method based on a hiring method that was successful in the past. For example, the collection unit retrieves a company's past hiring history from a database and analyzes it using data mining technology. The collection unit can also optimize an information collection method for a specific skill set based on the past hiring history. For example, the collection unit compares the skill sets of previously hired workers with the company's job information and selects the optimal information collection method. The collection unit can also analyze the past hiring history and select the most effective information collection channel. For example, the collection unit selects similar channels based on information collection channels that were effective in the past (e.g., job sites, social media, etc.). In this way, the optimal information collection method can be selected by analyzing the past hiring history. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input a company's past hiring history data into a generation AI and have the generation AI select the optimal information collection method.

[0066] When collecting information, the collection unit can filter the information based on the company's current project or business situation. For example, the collection unit prioritizes collecting information about workers with skills related to ongoing projects. For example, the collection unit acquires ongoing project information from the company's project management system and filters information about workers with related skill sets. The collection unit can also filter information about workers with required skill sets according to the business situation. For example, the collection unit acquires business situation data from the company's business management system and filters information about workers with required skill sets. The collection unit can also collect information about workers with specific experience based on the company's business situation. For example, the collection unit analyzes the company's business situation data and prioritizes collecting information about workers with specific project experience. This allows more relevant information to be collected by filtering information based on the company's current project or business situation. Some or all of the above-mentioned processing by the collection unit may be performed using, for example, AI, or may be performed without AI. For example, the collection unit can input the company's business situation data into the generation AI and cause the generation AI to perform the filtering process.

[0067] The collection unit can estimate the user's emotions and determine the priority of information to be collected based on the estimated user emotions. For example, when the user is stressed, the collection unit prioritizes collecting information of high importance. For example, the collection unit captures the user's facial expressions with a camera and estimates the user's emotions using an emotion estimation algorithm. The collection unit can also collect detailed information when the user is relaxed. For example, the collection unit records the user's voice and estimates the user's emotions using voice analysis technology. The collection unit can also prioritize information that can be collected quickly when the user is in a hurry. For example, the collection unit collects the user's biometric data (heart rate and electrodermal activity) using a sensor and estimates the user's emotions using an emotion estimation algorithm. This enables more appropriate information collection by prioritizing information according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit may input image data of a user taken with a camera to the generation AI and cause the generation AI to estimate the user's emotions.

[0068] When collecting information, the collection unit can prioritize collecting highly relevant information by taking into account the geographic location information of the company. The collection unit, for example, prioritizes collecting information about workers close to the company's location. For example, the collection unit obtains company location information from a geographic information system (GIS) and filters information about nearby workers. The collection unit can also filter and collect geographically relevant information. For example, the collection unit prioritizes collecting information about workers in a specific area based on the company's location. The collection unit can also collect information about workers in a specific area based on the company's geographic location. For example, the collection unit analyzes company location information and prioritizes collecting information about workers in a specific area. This makes it possible to prioritize collecting highly relevant information by taking into account the company's geographic location information. Some or all of the above-described processing in the collection unit may be performed using AI, for example, or may be performed without using AI. For example, the collection unit can input company location information into the generation AI and cause the generation AI to filter highly relevant information.

[0069] When collecting information, the collection unit can analyze the company's social media activities and collect related information. For example, the collection unit can collect information related to a specific skill set from the company's social media activities. For example, the collection unit can obtain posts from the company's social media accounts and extract related information using text analysis technology. The collection unit can also analyze the company's activities on social media and collect information on related workers. For example, the collection unit can analyze the company's social media activity data and collect information on workers with a specific skill set. The collection unit can also collect information on workers with specific experience based on the company's social media activities. For example, the collection unit can analyze the company's social media activity data and collect information on workers with specific project experience. In this way, related information can be collected by analyzing the company's social media activities. Some or all of the above-mentioned processing in the collection unit can be performed using, for example, AI, or can be performed without using AI. For example, the collection unit can input the company's social media activity data into the generation AI and cause the generation AI to collect related information.

[0070] The analysis unit can estimate the user's emotions and adjust the presentation method of the analysis based on the estimated user emotions. For example, if the user is nervous, the analysis unit provides simple, highly visible analysis results. For example, the analysis unit captures the user's facial expressions with a camera and estimates the emotions using an emotion estimation algorithm. The analysis unit can also provide detailed analysis results if the user is relaxed. For example, the analysis unit records the user's voice and estimates the emotions using voice analysis technology. The analysis unit can also provide concise analysis results if the user is in a hurry. For example, the analysis unit collects the user's biometric data (heart rate and electrodermal activity) with a sensor and estimates the emotions using an emotion estimation algorithm. This allows the analysis method to be adjusted according to the user's emotions, thereby providing more appropriate analysis results. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI can be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit may input image data of a user taken with a camera to the generation AI and cause the generation AI to estimate the user's emotions.

[0071] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the information. For example, the analysis unit performs a detailed analysis on information with high importance. For example, the analysis unit evaluates the importance of a company's job information and performs a detailed analysis on the information with high importance. The analysis unit can also perform a simplified analysis on information with low importance. For example, the analysis unit evaluates the importance of worker resume information and performs a simplified analysis on the information with low importance. The analysis unit can also dynamically adjust the level of detail of the analysis according to the importance of the information. For example, the analysis unit evaluates the importance of a company's job information and worker resume information and adjusts the level of detail of the analysis according to the importance. This enables more appropriate analysis by adjusting the level of detail of the analysis based on the importance of the information. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the company's job information and the worker's resume information into a generation AI and have the generation AI adjust the level of detail of the analysis.

[0072] The analysis unit can apply different analysis algorithms depending on the category of information during analysis. For example, the analysis unit applies a specific analysis algorithm to skill information. For example, the analysis unit analyzes the worker's skill information using a clustering algorithm. The analysis unit can also apply different analysis algorithms to experience information. For example, the analysis unit analyzes the worker's experience information using regression analysis. The analysis unit can also apply a dedicated analysis algorithm to the desired work location information. For example, the analysis unit analyzes the worker's desired work location information using a classification algorithm. This enables more appropriate analysis by applying different analysis algorithms depending on the category of information. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the worker's skill information, experience information, and desired work location information into the generation AI and cause the generation AI to apply different analysis algorithms.

[0073] The analysis unit can estimate the user's emotions and adjust the length of the analysis based on the estimated user emotions. For example, if the user is in a hurry, the analysis unit can provide a short and concise analysis result. For example, the analysis unit can capture the user's facial expressions with a camera and estimate the emotion using an emotion estimation algorithm. The analysis unit can also provide a detailed analysis result if the user is relaxed. For example, the analysis unit can record the user's voice and estimate the emotion using voice analysis technology. The analysis unit can also provide a visually stimulating analysis result if the user is excited. For example, the analysis unit can collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotion using an emotion estimation algorithm. This allows the length of the analysis to be adjusted according to the user's emotions, thereby providing more appropriate analysis results. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI can be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit may input image data of a user taken with a camera to the generation AI and cause the generation AI to estimate the user's emotions.

[0074] During analysis, the analysis unit can determine the priority of analysis based on the time of information submission. The analysis unit, for example, prioritizes the analysis of the most recent information. For example, the analysis unit evaluates the time of submission of a company's job information and prioritizes the analysis of the most recent information. The analysis unit can also perform a simplified analysis on older information. For example, the analysis unit evaluates the time of submission of a worker's resume information and performs a simplified analysis on older information. The analysis unit can also dynamically adjust the priority of analysis based on the time of submission. For example, the analysis unit evaluates the time of submission of a company's job information and a worker's resume information and adjusts the priority of analysis based on the time of submission. This enables more appropriate analysis by determining the priority of analysis based on the time of submission of the information. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without AI. For example, the analysis unit can input the company's job information and the worker's resume information into the generation AI and have the generation AI determine the priority of analysis.

[0075] During analysis, the analysis unit can adjust the order of analysis based on the relevance of the information. For example, the analysis unit prioritizes analysis of highly relevant information. For example, the analysis unit evaluates the relevance between a company's job information and the worker's resume information, and prioritizes analysis of the highly relevant information. The analysis unit can also perform a simplified analysis on less relevant information. For example, the analysis unit evaluates the relevance between a company's job information and the worker's resume information, and performs a simplified analysis on the less relevant information. The analysis unit can also dynamically adjust the order of analysis based on the relevance of the information. For example, the analysis unit evaluates the relevance between a company's job information and the worker's resume information, and adjusts the order of analysis based on the relevance. This enables more appropriate analysis by adjusting the order of analysis based on the relevance of the information. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the company's job information and the worker's resume information into the generation AI and cause the generation AI to adjust the order of analysis.

[0076] The determination unit can estimate the user's emotions and adjust the criteria for determination based on the estimated user emotions. For example, if the user is nervous, the determination unit makes a determination using strict criteria. For example, the determination unit captures the user's facial expression with a camera and estimates the emotion using an emotion estimation algorithm. Furthermore, if the user is relaxed, the determination unit can also make a determination using flexible criteria. For example, the determination unit records the user's voice and estimates the emotion using voice analysis technology. Furthermore, if the user is in a hurry, the determination unit can make a quick determination. For example, the determination unit collects the user's biometric data (heart rate and electrodermal activity) using a sensor and estimates the emotion using an emotion estimation algorithm. This allows for more appropriate determination by adjusting the criteria for determination based on the user's emotions. The emotion estimation is realized using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the determination unit may be performed using, for example, AI, or may be performed without using AI. For example, the determination unit may input image data of a user taken with a camera to the generation AI and cause the generation AI to estimate the user's emotions.

[0077] The determination unit can improve the accuracy of the determination by taking into account the interrelationship between information when making a determination. The determination unit makes a determination by taking into account, for example, the interrelationship between skills and experience. For example, the determination unit evaluates the interrelationship between skills and experience in a company's job information and the worker's resume information, and makes a determination. The determination unit can also make a determination by taking into account the interrelationship between a desired work location and skills. For example, the determination unit evaluates the interrelationship between a desired work location and skills in a company's job information and the worker's resume information, and makes a determination. The determination unit can also improve the accuracy of the determination based on the interrelationship between information. For example, the determination unit evaluates the interrelationship between a company's job information and the worker's resume information, and improves the accuracy of the determination. In this way, the accuracy of the determination is improved by taking into account the interrelationship between information. Some or all of the above-described processing in the determination unit may be performed using, for example, AI, or may be performed without using AI. For example, the determination unit can input the company's job information and the worker's resume information into a generation AI, and cause the generation AI to evaluate the interrelationship between the information.

[0078] When making a judgment, the judgment unit can make a judgment taking into account attribute information of the person submitting the information. The judgment unit makes a judgment taking into account, for example, the age of the person submitting the information. For example, the judgment unit evaluates the age of the person submitting the company's job information and the worker's resume information and makes a judgment. The judgment unit can also make a judgment taking into account the gender of the person submitting the information. For example, the judgment unit evaluates the gender of the person submitting the company's job information and the worker's resume information and makes a judgment. The judgment unit can also make a judgment taking into account the work history of the person submitting the information. For example, the judgment unit evaluates the work history of the person submitting the company's job information and the worker's resume information and makes a judgment. This enables a more appropriate judgment by taking into account the attribute information of the person submitting the information. Some or all of the above-mentioned processing in the judgment unit may be performed using, for example, AI, or may be performed without using AI. For example, the judgment unit can input the company's job information and the worker's resume information into a generation AI and cause the generation AI to evaluate the attribute information of the person submitting the information.

[0079] The determination unit can estimate the user's emotions and adjust the display order of the determination results based on the estimated user emotions. For example, if the user is nervous, the determination unit displays important results first. For example, the determination unit captures the user's facial expressions with a camera and estimates the user's emotions using an emotion estimation algorithm. Furthermore, if the user is relaxed, the determination unit can sequentially display detailed results. For example, the determination unit records the user's voice and estimates the user's emotions using voice analysis technology. Furthermore, if the user is in a hurry, the determination unit can display results that highlight the key points first. For example, the determination unit collects the user's biometric data (heart rate and electrodermal activity) using a sensor and estimates the user's emotions using an emotion estimation algorithm. This allows for more appropriate display of results by adjusting the display order of the determination results according to the user's emotions. Emotion estimation is realized using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the determination unit may be performed using, for example, AI, or may be performed without using AI. For example, the determination unit may input image data of a user taken with a camera to the generation AI and cause the generation AI to estimate the user's emotions.

[0080] The determination unit can make a determination taking into account the geographical distribution of information. For example, the determination unit prioritizes geographically close information. For example, the determination unit evaluates the geographical distribution of company job information and worker resume information, and prioritizes geographically close information. The determination unit can also make a determination taking into account information that is highly geographically related. For example, the determination unit evaluates the geographical distribution of company job information and worker resume information, and makes a determination taking into account the highly related information. The determination unit can also improve the accuracy of the determination based on the geographical distribution. For example, the determination unit evaluates the geographical distribution of company job information and worker resume information, and improves the accuracy of the determination. This enables a more appropriate determination by taking the geographical distribution of the information into account. Some or all of the above-described processing in the determination unit may be performed using, for example, AI, or may be performed without using AI. For example, the determination unit can input company job information and worker resume information into a generation AI and cause the generation AI to evaluate the geographical distribution.

[0081] The determination unit can improve the accuracy of the determination by referring to related literature for the information when making the determination. The determination unit, for example, determines the compatibility of skills based on related literature. For example, the determination unit evaluates the compatibility of skills between a company's job information and the worker's resume information, and makes the determination by referring to related literature. The determination unit can also determine the compatibility of experience based on related literature. For example, the determination unit evaluates the compatibility of experience between a company's job information and the worker's resume information, and makes the determination by referring to related literature. The determination unit can also improve the accuracy of the determination by referring to related literature. For example, the determination unit evaluates the compatibility of related literature for a company's job information and the worker's resume information, and improves the accuracy of the determination. As a result, the accuracy of the determination is improved by referring to related literature for the information. Some or all of the above-described processing in the determination unit may be performed using, for example, AI, or may be performed without using AI. For example, the determination unit can input the company's job information and the worker's resume information into the generation AI and cause the generation AI to refer to related literature.

[0082] The notification unit can estimate the user's emotions and adjust the notification display method based on the estimated user emotions. For example, if the user is nervous, the notification unit provides a simple, highly visible display method. For example, the notification unit captures the user's facial expression with a camera and estimates the emotion using an emotion estimation algorithm. Furthermore, if the user is relaxed, the notification unit can provide a display method that includes detailed information. For example, the notification unit records the user's voice and estimates the emotion using voice analysis technology. Furthermore, if the user is in a hurry, the notification unit can provide a display method that focuses on the key points. For example, the notification unit collects the user's biometric data (heart rate and electrodermal activity) using a sensor and estimates the emotion using an emotion estimation algorithm. This allows for more appropriate notifications by adjusting the notification display method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the notification unit may be performed using, for example, AI, or may be performed without using AI. For example, the notification unit may input image data of the user taken with a camera to the generation AI and cause the generation AI to estimate the user's emotions.

[0083] The notification unit can select the optimal notification method by referring to past notification history when making a notification. For example, the notification unit may perform notification using a similar method based on a notification method that was effective in the past. For example, the notification unit may retrieve past notification history from a database and analyze it using data mining technology. The notification unit can also optimize a specific notification method from the past notification history. For example, the notification unit may perform notification using a similar method based on a notification method that was effective in the past. The notification unit can also analyze past notification history and select the most effective notification channel. For example, the notification unit may select a similar channel based on a notification channel that was effective in the past (e.g., email notification, push notification, etc.). In this way, the optimal notification method can be selected by referring to the past notification history. Some or all of the above-described processing in the notification unit may be performed using, or without, AI. For example, the notification unit may input past notification history data into a generation AI and have the generation AI select the optimal notification method.

[0084] The notification unit can determine the priority of notifications based on the importance of the information when sending notifications. For example, the notification unit prioritizes notification of information with high importance. For example, the notification unit evaluates the importance of a company's job information and prioritizes notification of information with high importance. The notification unit can also provide a simplified notification for information with low importance. For example, the notification unit evaluates the importance of worker resume information and provides a simplified notification for information with low importance. The notification unit can also dynamically adjust the priority of notifications according to the importance of the information. For example, the notification unit evaluates the importance of a company's job information and worker resume information and adjusts the priority of notifications according to the importance. This enables more appropriate notifications by determining the priority of notifications based on the importance of the information. Some or all of the above-described processing in the notification unit may be performed using, for example, AI, or may be performed without using AI. For example, the notification unit can input the company's job information and worker resume information into a generation AI and have the generation AI determine the priority of notifications.

[0085] The notification unit can estimate the user's emotions and adjust the content of the notification based on the estimated user emotions. For example, if the user is nervous, the notification unit provides concise and to-the-point notification content. For example, the notification unit captures the user's facial expression with a camera and estimates the user's emotions using an emotion estimation algorithm. Furthermore, if the user is relaxed, the notification unit can provide detailed notification content. For example, the notification unit records the user's voice and estimates the user's emotions using voice analysis technology. Furthermore, if the user is in a hurry, the notification unit can provide notification content that is easy to understand. For example, the notification unit collects the user's biometric data (heart rate and electrodermal activity) using a sensor and estimates the user's emotions using an emotion estimation algorithm. This allows for more appropriate notification by adjusting the content of the notification according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the notification unit may be performed using, for example, AI, or may be performed without using AI. For example, the notification unit may input image data of the user taken with a camera to the generation AI and cause the generation AI to estimate the user's emotions.

[0086] The notification unit can select the optimal notification method by taking into account the user's device information when providing a notification. For example, if the user is using a smartphone, the notification unit provides a push notification. For example, the notification unit acquires the user's device information and provides a push notification when the user is using a smartphone. The notification unit can also provide a notification optimized for a large screen when the user is using a tablet. For example, the notification unit acquires the user's device information and provides a notification optimized for a large screen when the user is using a tablet. The notification unit can also provide a concise and highly visible notification when the user is using a smartwatch. For example, the notification unit acquires the user's device information and provides a concise and highly visible notification when the user is using a smartwatch. This allows the optimal notification method to be selected by taking into account the user's device information. Some or all of the above-described processing in the notification unit may be performed using, for example, AI, or may be performed without using AI. For example, the notification unit can input the user's device information into the generation AI and cause the generation AI to select the optimal notification method.

[0087] The notification unit may analyze the user's social media activity and provide relevant notifications at the time of notification. The notification unit may, for example, notify the user of relevant job information based on the user's social media activity. For example, the notification unit may acquire posts from the user's social media account and extract relevant job information using text analysis technology. The notification unit may also analyze the user's activity on social media and provide relevant company information. For example, the notification unit may analyze the user's social media activity data and provide relevant company information. The notification unit may also provide notifications related to a specific skill set based on the user's social media activity. For example, the notification unit may analyze the user's social media activity data and provide notifications related to a specific skill set. In this way, relevant notifications can be provided by analyzing the user's social media activity. Some or all of the above-described processing in the notification unit may be performed using, for example, AI, or may be performed without using AI. For example, the notification unit may input the user's social media activity data into a generation AI and cause the generation AI to generate relevant notifications.

[0088] The soft skill analysis unit can estimate the user's emotions and adjust the soft skill analysis method based on the estimated user emotions. For example, if the user is nervous, the soft skill analysis unit provides simple, highly visible analysis results. For example, the soft skill analysis unit captures the user's facial expressions with a camera and estimates their emotions using an emotion estimation algorithm. The soft skill analysis unit can also provide detailed analysis results if the user is relaxed. For example, the soft skill analysis unit records the user's voice and estimates their emotions using voice analysis technology. The soft skill analysis unit can also provide concise analysis results if the user is in a hurry. For example, the soft skill analysis unit collects the user's biometric data (heart rate and electrodermal activity) using a sensor and estimates their emotions using an emotion estimation algorithm. This enables more appropriate analysis by adjusting the soft skill analysis method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI can be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processing in the soft skill analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the soft skill analysis unit may input image data of a user taken with a camera to the generation AI and have the generation AI estimate the user's emotions.

[0089] The soft skills analysis unit can optimize the analysis algorithm by referring to past soft skill data during soft skill analysis. The soft skills analysis unit, for example, selects an optimal analysis algorithm based on past soft skill data. For example, the soft skills analysis unit retrieves past soft skill data from a database and analyzes it using data mining technology. The soft skills analysis unit can also analyze past soft skill data and optimize the analysis algorithm for a specific skill set. For example, the soft skills analysis unit selects an optimal analysis algorithm based on soft skill data evaluated in the past. The soft skills analysis unit can also improve the accuracy of the analysis by referring to past soft skill data. For example, the soft skills analysis unit analyzes past soft skill data and improves the accuracy of the analysis algorithm. In this way, the analysis algorithm can be optimized by referring to past soft skill data. Some or all of the above-described processing in the soft skills analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the soft skills analysis unit can input past soft skill data into a generation AI and cause the generation AI to optimize the analysis algorithm.

[0090] The soft skill analysis unit can estimate the user's emotions and prioritize soft skills based on the estimated user emotions. For example, if the user is nervous, the soft skill analysis unit prioritizes analysis of important soft skills. For example, the soft skill analysis unit captures the user's facial expressions with a camera and estimates their emotions using an emotion estimation algorithm. Furthermore, if the user is relaxed, the soft skill analysis unit can analyze detailed soft skills. For example, the soft skill analysis unit records the user's voice and estimates their emotions using voice analysis technology. Furthermore, if the user is in a hurry, the soft skill analysis unit can prioritize soft skills that can be analyzed quickly. For example, the soft skill analysis unit collects the user's biometric data (heart rate and electrodermal activity) using a sensor and estimates their emotions using an emotion estimation algorithm. This enables more appropriate analysis by prioritizing soft skills according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI can be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processing in the soft skill analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the soft skill analysis unit may input image data of a user taken with a camera to the generation AI and have the generation AI estimate the user's emotions.

[0091] The soft skill analysis unit can perform soft skill analysis while taking into account the user's geographic location information. The soft skill analysis unit, for example, analyzes relevant soft skills based on the user's location. For example, the soft skill analysis unit obtains the user's geographic location information from a geographic information system (GIS) and analyzes relevant soft skills. The soft skill analysis unit can also prioritize analysis of soft skills with high geographic relevance. For example, the soft skill analysis unit prioritizes analysis of soft skills in a specific region based on the user's geographic location information. The soft skill analysis unit can also analyze soft skills in a specific region based on the user's geographic location information. For example, the soft skill analysis unit analyzes the user's geographic location information and prioritizes analysis of soft skills in a specific region. This enables more appropriate soft skill analysis by taking the user's geographic location information into consideration. Some or all of the above-described processing in the soft skill analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the soft skill analysis unit can input the user's geographic location information to a generation AI and cause the generation AI to perform a soft skill analysis.

[0092] The feedback unit can estimate the user's emotions and adjust the feedback display method based on the estimated user emotions. For example, if the user is nervous, the feedback unit provides a simple, highly visible display method. For example, the feedback unit captures the user's facial expression with a camera and estimates the emotion using an emotion estimation algorithm. Furthermore, if the user is relaxed, the feedback unit can provide a display method that includes detailed information. For example, the feedback unit records the user's voice and estimates the emotion using voice analysis technology. Furthermore, if the user is in a hurry, the feedback unit can provide a display method that focuses on the main points. For example, the feedback unit collects the user's biometric data (heart rate and electrodermal activity) with a sensor and estimates the emotion using an emotion estimation algorithm. This enables more appropriate feedback by adjusting the feedback display method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the feedback unit may be performed using, for example, AI, or may be performed without using AI. For example, the feedback unit may input image data of the user taken by a camera to the generation AI and cause the generation AI to estimate the user's emotions.

[0093] When providing feedback, the feedback unit can select an optimal feedback method by referring to past feedback history. The feedback unit, for example, provides feedback using a similar method based on feedback methods that were effective in the past. For example, the feedback unit retrieves past feedback history from a database and analyzes it using data mining technology. The feedback unit can also optimize a specific feedback method from the past feedback history. For example, the feedback unit provides feedback using a similar method based on feedback methods that were effective in the past. The feedback unit can also analyze the past feedback history and select the most effective feedback channel. For example, the feedback unit selects a similar channel based on feedback channels that were effective in the past (e.g., email notification, push notification, etc.). In this way, the optimal feedback method can be selected by referring to the past feedback history. Some or all of the above-described processing in the feedback unit may be performed using, or without, AI. For example, the feedback unit can input past feedback history data to a generation AI and cause the generation AI to select an optimal feedback method.

[0094] The feedback unit can estimate the user's emotions and adjust the feedback content based on the estimated user emotions. For example, if the user is nervous, the feedback unit provides concise and to-the-point feedback. For example, the feedback unit captures the user's facial expression with a camera and estimates the user's emotions using an emotion estimation algorithm. Furthermore, if the user is relaxed, the feedback unit can provide detailed feedback. For example, the feedback unit records the user's voice and estimates the user's emotions using voice analysis technology. Furthermore, if the user is in a hurry, the feedback unit can provide feedback that is easy to understand quickly. For example, the feedback unit collects the user's biometric data (heart rate and electrodermal activity) using a sensor and estimates the user's emotions using an emotion estimation algorithm. This enables more appropriate feedback by adjusting the feedback content according to the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the feedback unit may be performed using, for example, AI, or may be performed without using AI. For example, the feedback unit may input image data of the user taken by a camera to the generation AI and cause the generation AI to estimate the user's emotions.

[0095] When providing feedback, the feedback unit can select the optimal feedback method by taking into account the user's device information. For example, if the user is using a smartphone, the feedback unit provides feedback via push notification. For example, the feedback unit acquires the user's device information and provides feedback via push notification when the user is using a smartphone. Furthermore, if the user is using a tablet, the feedback unit can provide feedback optimized for a large screen. For example, the feedback unit acquires the user's device information and provides feedback optimized for a large screen when the user is using a tablet. Furthermore, the feedback unit can provide concise and highly visible feedback when the user is using a smartwatch. For example, the feedback unit acquires the user's device information and provides concise and highly visible feedback when the user is using a smartwatch. This allows the optimal feedback method to be selected by taking into account the user's device information. Some or all of the above-described processing in the feedback unit may be performed using, for example, AI, or may be performed without using AI. For example, the feedback unit can input the user's device information to the generation AI and cause the generation AI to select the optimal feedback method. === Hard Collateral 1-1 === Each of the multiple elements, including the collection unit, analysis unit, determination unit, notification unit, soft skill analysis unit, and feedback unit, described above, is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the collection unit is realized by the control unit 46A of the smart device 14 and collects information on companies and workers. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the collected information. The determination unit is realized by the specific processing unit 290 of the data processing device 12 and calculates a matching degree based on the analyzed information. The notification unit is realized by the control unit 46A of the smart device 14 and issues a notification based on the matching degree. The soft skill analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the soft skills of the worker. The feedback unit is realized by the specific processing unit 290 of the data processing device 12 and provides detailed feedback on the matching degree. === Hard Collateral 1-2 === Each of the multiple elements, including the collection unit, analysis unit, determination unit, notification unit, soft skill analysis unit, and feedback unit, described above, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the collection unit is realized by the control unit 46A of the smart glasses 214 and collects information on companies and workers. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the collected information. The determination unit is realized by the specific processing unit 290 of the data processing device 12 and calculates a matching degree based on the analyzed information. The notification unit is realized by the control unit 46A of the smart glasses 214 and issues a notification based on the matching degree. The soft skill analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the soft skills of the workers. The feedback unit is realized by the specific processing unit 290 of the data processing device 12 and provides detailed feedback on the matching degree. === Hard Collateral 1-3 === Each of the multiple elements, including the collection unit, analysis unit, determination unit, notification unit, soft skill analysis unit, and feedback unit, described above, is realized, for example, by at least one of the headset type terminal 314 and the data processing device 12. For example, the collection unit is realized by the control unit 46A of the headset type terminal 314 and collects information on companies and workers. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the collected information. The determination unit is realized by the specific processing unit 290 of the data processing device 12 and calculates the degree of matching based on the analyzed information. The notification unit is realized by the control unit 46A of the headset type terminal 314 and issues a notification based on the degree of matching. The soft skill analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the soft skills of the worker. The feedback unit is realized by the specific processing unit 290 of the data processing device 12 and provides detailed feedback on the degree of matching. === Hard Collateral 1-4 === Each of the multiple elements, including the collection unit, analysis unit, determination unit, notification unit, soft skill analysis unit, and feedback unit, described above, is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the collection unit is realized by the control unit 46A of the robot 414 and collects information on companies and workers. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the collected information. The determination unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and calculates the degree of matching based on the analyzed information. The notification unit is realized, for example, by the control unit 46A of the robot 414 and issues a notification based on the degree of matching. The soft skill analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and analyzes the soft skills of the worker. The feedback unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and provides detailed feedback on the degree of matching.

[0096] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0097] The analysis unit can estimate the user's emotions and determine the analysis priority based on the estimated user emotions. For example, if the user is feeling stressed, it prioritizes analysis of information of high importance. For example, the analysis unit can capture the user's facial expression with a camera and estimate the emotion using an emotion estimation algorithm. Also, if the user is relaxed, it can analyze more detailed information. For example, the analysis unit can record the user's voice and estimate the emotion using voice analysis technology. Also, if the user is in a hurry, it can prioritize information that can be analyzed quickly. For example, the analysis unit can collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotion using an emotion estimation algorithm. This enables more appropriate analysis by determining the analysis priority according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the analysis unit can be performed using, for example, AI, or without AI. For example, the analysis unit can input image data of a user taken with a camera into the generation AI and have the generation AI estimate the user's emotions.

[0098] The determination unit can estimate the user's emotions and adjust the criteria for determination based on the estimated user emotions. For example, if the user is nervous, the determination unit uses strict criteria. For example, the determination unit captures the user's facial expression with a camera and estimates the emotion using an emotion estimation algorithm. Alternatively, if the user is relaxed, the determination unit can use more flexible criteria. For example, the determination unit records the user's voice and estimates the emotion using voice analysis technology. Alternatively, if the user is in a hurry, the determination unit can quickly make a determination. For example, the determination unit collects the user's biometric data (heart rate and electrodermal activity) with a sensor and estimates the emotion using an emotion estimation algorithm. This allows for more appropriate determination by adjusting the criteria for determination based on the user's emotions. The emotion estimation is realized using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the determination unit may be performed using, for example, AI, or without AI. For example, the judgment unit can input image data of a user taken with a camera into the generation AI and have the generation AI estimate the user's emotions.

[0099] The notification unit can estimate the user's emotions and adjust the notification display method based on the estimated user emotions. For example, if the user is nervous, a simple, highly visible display method can be provided. For example, the notification unit can capture the user's facial expression with a camera and estimate the emotion using an emotion estimation algorithm. Also, if the user is relaxed, a display method including detailed information can be provided. For example, the notification unit can record the user's voice and estimate the emotion using voice analysis technology. Also, if the user is in a hurry, a display method that focuses on the main points can be provided. For example, the notification unit can collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotion using an emotion estimation algorithm. This allows for more appropriate notifications by adjusting the notification display method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the notification unit can be performed using, for example, AI, or without AI. For example, the notification unit can input image data of the user taken with a camera into the generation AI and have the generation AI estimate the user's emotions.

[0100] The feedback unit can estimate the user's emotions and adjust the feedback display method based on the estimated user emotions. For example, if the user is nervous, a simple, highly visible display method can be provided. For example, the feedback unit can capture the user's facial expressions with a camera and estimate the emotions using an emotion estimation algorithm. Alternatively, if the user is relaxed, a display method including more detailed information can be provided. For example, the feedback unit can record the user's voice and estimate the emotions using voice analysis technology. Alternatively, if the user is in a hurry, a display method that focuses on the main points can be provided. For example, the feedback unit can collect the user's biometric data (heart rate and electrodermal activity) using a sensor and estimate the emotions using an emotion estimation algorithm. This allows for more appropriate feedback by adjusting the feedback display method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the feedback unit can be performed using, for example, AI, or without AI. For example, the feedback unit can input image data of the user taken with a camera into the generation AI and have the generation AI estimate the user's emotions.

[0101] The soft skill analysis unit can estimate the user's emotions and adjust the soft skill analysis method based on the estimated user emotions. For example, if the user is nervous, it can provide simple, highly visible analysis results. For example, the soft skill analysis unit can capture the user's facial expressions with a camera and estimate their emotions using an emotion estimation algorithm. It can also provide detailed analysis results if the user is relaxed. For example, the soft skill analysis unit can record the user's voice and estimate their emotions using voice analysis technology. It can also provide analysis results that focus on the key points if the user is in a hurry. For example, the soft skill analysis unit can collect the user's biometric data (heart rate and electrodermal activity) using a sensor and estimate their emotions using an emotion estimation algorithm. This allows for more appropriate analysis by adjusting the soft skill analysis method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI can be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processing in the soft skill analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the soft skill analysis unit may input image data of a user taken with a camera to the generation AI and have the generation AI estimate the user's emotions.

[0102] The collection unit can analyze a company's past hiring history and select an appropriate information collection method. For example, the collection unit selects a similar information collection method based on a hiring method that was successful in the past. For example, the collection unit retrieves a company's past hiring history from a database and analyzes it using data mining technology. The collection unit can also optimize an information collection method for a specific skill set based on the past hiring history. For example, the collection unit compares the skill sets of previously hired workers with the company's job information and selects the optimal information collection method. The collection unit can also analyze the past hiring history and select the most effective information collection channel. For example, the collection unit selects similar channels based on information collection channels that were effective in the past (e.g., job sites, social media, etc.). In this way, the optimal information collection method can be selected by analyzing the past hiring history. Some or all of the above-mentioned processing in the collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit can input a company's past hiring history data into the generation AI and have the generation AI select the optimal information collection method.

[0103] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the information. For example, detailed analysis is performed on information with high importance. For example, the analysis unit evaluates the importance of a company's job information and performs a detailed analysis on the information with high importance. The analysis unit can also perform a simplified analysis on information with low importance. For example, the analysis unit evaluates the importance of worker resume information and performs a simplified analysis on the information with low importance. The analysis unit can also dynamically adjust the level of detail of the analysis according to the importance of the information. For example, the analysis unit evaluates the importance of a company's job information and worker resume information and adjusts the level of detail of the analysis according to the importance. This enables more appropriate analysis by adjusting the level of detail of the analysis based on the importance of the information. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the company's job information and the worker's resume information into a generation AI and have the generation AI adjust the level of detail of the analysis.

[0104] The judgment unit can improve the accuracy of the judgment by taking into account the interrelationship between information when making a judgment. For example, the judgment is made by taking into account the interrelationship between skills and experience. For example, the judgment unit evaluates the interrelationship between skills and experience in the company's job information and the worker's resume information and makes a judgment. The judgment unit can also make a judgment by taking into account the interrelationship between desired work location and skills. For example, the judgment unit evaluates the interrelationship between desired work location and skills in the company's job information and the worker's resume information and makes a judgment. The judgment unit can also improve the accuracy of the judgment based on the interrelationship between information. For example, the judgment unit evaluates the interrelationship between the company's job information and the worker's resume information and improves the accuracy of the judgment. In this way, the accuracy of the judgment is improved by taking the interrelationship between information into account. Some or all of the above-mentioned processing in the judgment unit may be performed using, for example, AI, or may be performed without using AI. For example, the judgment unit can input the company's job information and the worker's resume information into a generation AI and cause the generation AI to evaluate the interrelationship between the information.

[0105] When sending a notification, the notification unit can select the optimal notification method by referring to past notification history. For example, the notification unit can send a notification using a similar method based on a notification method that was effective in the past. For example, the notification unit can retrieve past notification history from a database and analyze it using data mining technology. The notification unit can also optimize a specific notification method from the past notification history. For example, the notification unit can send a notification using a similar method based on a notification method that was effective in the past. The notification unit can also analyze past notification history and select the most effective notification channel. For example, the notification unit can select a similar channel based on a notification channel that was effective in the past (e.g., email notification, push notification, etc.). In this way, the optimal notification method can be selected by referring to the past notification history. Some or all of the above-mentioned processing in the notification unit may be performed using, for example, AI, or may be performed without using AI. For example, the notification unit can input past notification history data to a generation AI and have the generation AI select the optimal notification method.

[0106] When providing feedback, the feedback unit can select the optimal feedback method by taking into account the user's device information. For example, if the user is using a smartphone, the feedback unit provides feedback via push notification. For example, the feedback unit acquires the user's device information and provides feedback via push notification when the user is using a smartphone. Furthermore, if the user is using a tablet, the feedback unit can provide feedback optimized for a large screen. For example, the feedback unit acquires the user's device information and provides feedback optimized for a large screen when the user is using a tablet. Furthermore, the feedback unit can provide concise and highly visible feedback when the user is using a smartwatch. For example, the feedback unit acquires the user's device information and provides concise and highly visible feedback when the user is using a smartwatch. This allows the optimal feedback method to be selected by taking into account the user's device information. Some or all of the above-described processing in the feedback unit may be performed using, for example, AI, or may be performed without using AI. For example, the feedback unit can input the user's device information into the generation AI and cause the generation AI to select the optimal feedback method.

[0107] The processing flow of the second embodiment will be briefly explained below.

[0108] Step 1: The collection unit collects information about companies and workers. This information includes, for example, resume information, company job information, and skill sets. The collection unit uses web scraping technology to collect information from the Internet. It can also automatically obtain company job information using API integration. It can also collect information directly from companies and workers by manually entering it. Step 2: The analysis unit analyzes the information collected by the collection unit. This analysis is carried out using data mining and text analysis techniques. For example, the analysis may analyze a company's job postings and the employee's resume, and use statistical analysis techniques to evaluate the degree of match between the company's and the employee's skill sets. Step 3: The determination unit calculates the degree of matching based on the information analyzed by the analysis unit. The degree of matching is calculated using a scoring algorithm. For example, the degree of match between the skills required by the company and the skills of the worker is scored, and the degree of match between the experience required by the company and the experience of the worker is evaluated based on weighting criteria. Step 4: The notification unit issues a notification based on the matching degree calculated by the determination unit. Notifications are sent via email or push notification. For example, an email notification stating "This worker meets your requirements" is sent to the company, and a push notification stating "This company matches your skills and preferences" is sent to the worker.

[0109] 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 a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the 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.

[0110] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of the generative AI include a neural network (NN) and a neural network (NN). The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, image data, etc. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and may perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-mentioned parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. The processing performed by an AI including the generative AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI including the generative AI.

[0111] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, 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.

[0112] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0113] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0114] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0115] 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, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0116] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0117] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0118] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0119] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0120] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0121] 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 in accordance with the specific processing program 56 executed on the RAM 30.

[0122] The storage 32 stores a data generation model 58 and an 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0123] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. 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 the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0124] Note that a device other than the data processing device 12 may 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 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0125] 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 a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0126] The data generation model 58 is a so-called generative AI. An example of the 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 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, 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), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0127] 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 executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0128] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0129] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0130] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0131] 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, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0132] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0133] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0134] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0135] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0136] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0137] 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 in accordance with the specific processing program 56 executed on the RAM 30.

[0138] The storage 32 stores a data generation model 58 and an 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0139] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0140] Note that a device other than the data processing device 12 may 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 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0141] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0142] The data generation model 58 is a so-called generative AI. An example of the 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 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, 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), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0143] 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 executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0144] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0145] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0146] 7, a 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.

[0147] 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, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0148] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0149] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0150] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0151] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0152] The control 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 emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0153] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0154] 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 in accordance with the specific processing program 56 executed on the RAM 30.

[0155] The storage 32 stores a data generation model 58 and an 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0156] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0157] Note that a device other than the data processing device 12 may 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 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0158] 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 control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0159] The data generation model 58 is a so-called generative AI. An example of the 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 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, 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), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0160] 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 executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0161] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0162] The emotion identification model 59 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 an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0163] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0164] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0165] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0166] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0167] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs 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 a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0168] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0169] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0170] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0171] 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.

[0172] It is not necessary to store all 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 all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0173] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0174] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with 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). Also, the hardware resource that executes the specific process may be a single processor.

[0175] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0176] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0177] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0178] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, in order to avoid confusion and to facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0179] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[0180] [Explanation of symbols]

[0181] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. A collection department that collects information on companies and workers, an analysis unit that analyzes the information collected by the collection unit; a determination unit that calculates a matching degree based on the information analyzed by the analysis unit; a notification unit that issues a notification based on the matching degree calculated by the determination unit; Equipped with A system characterized by:

2. Equipped with a soft skills analysis department to analyze soft skills 2. The system of claim 1.

3. A feedback section is provided to provide detailed feedback on the degree of matching.

2. The system of claim 1.

4. The collecting unit Estimate the user's emotions and adjust the timing of information collection based on the estimated user emotions.

2. The system of claim 1.

5. The collecting unit Analyze the company's past hiring history and select the appropriate information gathering method 2. The system of claim 1.

6. The collecting unit When gathering information, filter it based on the company's current project or business situation.

2. The system of claim 1.

7. The collecting unit Estimate the user's emotions and prioritize the information to be collected based on the estimated user emotions.

2. The system of claim 1.

8. The collecting unit When collecting information, prioritize collecting the most relevant information by taking into account the geographic location of the company.

2. The system of claim 1.

9. The collecting unit When collecting information, analyze the company's social media activity and collect relevant information.

2. The system of claim 1.

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