system

Abstract

The system according to the embodiment aims to propose the most suitable job type and task based on an individual's skills, experience, and interests, and to appropriately allocate personnel. [Solution] A system according to an embodiment includes a collection unit, an analysis unit, a proposal unit, an allocation unit, and a selection unit. The collection unit collects individual skills, experience, and interests. The analysis unit analyzes the data collected by the collection unit. The proposal unit proposes job types and tasks based on the analysis results obtained by the analysis unit. The allocation unit allocates personnel based on the proposed job types and tasks. The selection unit selects a contractor based on information about the allocated personnel.

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 technologies do not adequately suggest the most suitable jobs and tasks based on an individual's skills, experience, and interests, and do not adequately allocate personnel appropriately, so there is room for improvement.

[0005] The system according to the embodiment aims to propose the most suitable job type and task based on an individual's skills, experience, and interests, and to appropriately allocate personnel. [Means for solving the problem]

[0006] The system according to the embodiment includes a collection unit, an analysis unit, a proposal unit, an allocation unit, and a selection unit. The collection unit collects individual skills, experience, and interests. The analysis unit analyzes the data collected by the collection unit. The proposal unit proposes job types and tasks based on the analysis results obtained by the analysis unit. The allocation unit allocates personnel based on the proposed job types and tasks. The selection unit selects a contractor based on information about the allocated personnel. [Effects of the Invention]

[0007] The system according to the embodiment can suggest the most suitable job type and task based on an individual's skills, experience, and interests, and can allocate personnel appropriately. [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 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 staffing system according to an embodiment of the present invention proposes job types and tasks suited to individuals and assigns appropriate personnel to necessary departments, thereby improving employee satisfaction (ES) and company productivity. This staffing system collects individuals' skills, experience, and interests, and uses AI to analyze these data. Based on the analysis results, the system then proposes the best job types and tasks for each individual. Furthermore, the system assigns appropriate personnel to necessary departments based on the proposed job types and tasks. This improves employee satisfaction and increases the company's overall productivity. Furthermore, when selecting outsourcing partners, it is possible to select companies that meet the desired criteria. For example, when collecting an individual's skills, experience, and interests, detailed information is collected about the individual's past work experience, skills, and interests, as well as their interests. For example, if an individual has experience in project management and is interested in data analysis, this information is collected. Next, AI analyzes the collected data. The AI ​​analyzes the individual's skills, experience, and interests to identify the best job types and tasks for each individual. For example, the system can propose data analysis project management to an individual with project management experience and an interest in data analysis. Based on the proposed job types and tasks, the system assigns appropriate personnel to necessary departments. For example, by assigning the right personnel to a department that needs to manage a data analysis project, the department's productivity can be improved. Furthermore, when selecting a contractor, it is possible to select a company that meets the desired criteria. For example, if personnel with specific skills or experience are needed, selecting a company that meets those criteria can improve work efficiency. This system improves employee satisfaction and increases the productivity of the entire company. For example, when individuals perform tasks that are suited to them, their motivation for work increases and work efficiency improves. Furthermore, by assigning the right personnel to the right department, the productivity of the entire department improves. Furthermore, when selecting a contractor, it is possible to improve work efficiency by selecting a company that meets the desired criteria. In this way, the personnel assignment system can improve employee satisfaction and increase the productivity of the entire company.

[0029] A staffing system according to an embodiment includes a collection unit, an analysis unit, a proposal unit, a placement unit, and a selection unit. The collection unit collects individual skills, experience, and interests. Examples of the individual skills, experience, and interests include, but are not limited to, technical skills, work experience, and hobbies. The collection unit, for example, collects the individual skills, experience, and interests using a questionnaire. The collection unit can also collect detailed information through interviews. The collection unit can also acquire the individual skills, experience, and interests from a database. For example, the collection unit conducts an online questionnaire to collect the individual skills, experience, and interests. The collection unit can also collect detailed information about the individual through interviews. The collection unit can also acquire the individual skills, experience, and interests from a database. The analysis unit analyzes the data collected by the collection unit and identifies a job type or task suitable for the individual. The analysis is performed using, for example, statistical analysis or a machine learning algorithm, but is not limited to, examples. For example, the analysis unit analyzes the collected data using statistical analysis to identify a job type or task suitable for the individual. The analysis unit can also analyze data using a machine learning algorithm to identify job types and tasks suitable for individuals. The analysis unit can also analyze data using data mining technology to identify job types and tasks suitable for individuals. The proposal unit proposes job types and tasks based on the analysis results obtained by the analysis unit. Proposals include, but are not limited to, proposals based on aptitude test results and proposals based on past success stories. For example, the proposal unit proposes job types and tasks based on aptitude test results. The proposal unit can also propose job types and tasks based on past success stories. The proposal unit can also use AI to propose job types and tasks optimal for individuals. The placement unit assigns personnel based on the job types and tasks proposed by the proposal unit. The placement is performed, for example, based on departmental needs or individual preferences, but is not limited to, examples. For example, the placement unit assigns personnel based on departmental needs. The placement unit can also assign personnel based on individual preferences. The placement unit can also perform optimal placement using AI. The selection unit selects a contractor based on information about the personnel assigned by the placement unit.The selection may be based on, for example, the company's evaluation criteria and suitability for the business, but is not limited to such examples. For example, the selection unit selects a business outsourcing partner based on the company's evaluation criteria. The selection unit can also select a business outsourcing partner based on suitability for the business. Furthermore, the selection unit can also select the optimal business outsourcing partner using AI. As a result, the staffing system according to the embodiment can improve employee satisfaction and improve the productivity of the entire company.

[0030] The collection unit can collect individual skills, experience, and interests. The collection unit, for example, collects individual skills, experience, and interests using a questionnaire. For example, the collection unit conducts an online questionnaire to collect individual skills, experience, and interests. The collection unit can also collect detailed information about individuals through interviews. For example, the collection unit collects individual skills, experience, and interests through interviews. The collection unit can also acquire individual skills, experience, and interests from a database. For example, the collection unit acquires individual skills, experience, and interests from a database. By collecting individual skills, experience, and interests in detail, more appropriate jobs and tasks can be suggested. Some or all of the above-described processing in the collection unit may be performed using, or without, AI. For example, the collection unit can input questionnaire response data into a generation AI and cause the generation AI to analyze the response data.

[0031] The analysis unit can analyze the collected data and identify the occupation or task suitable for the individual. The analysis unit can, for example, analyze the collected data using statistical analysis and identify the occupation or task suitable for the individual. For example, the analysis unit can analyze the collected data using statistical analysis and identify the occupation or task suitable for the individual. The analysis unit can also analyze the data using a machine learning algorithm and identify the occupation or task suitable for the individual. For example, the analysis unit can analyze the data using a machine learning algorithm and identify the occupation or task suitable for the individual. The analysis unit can also analyze the data using data mining technology and identify the occupation or task suitable for the individual. For example, the analysis unit can analyze the data using data mining technology and identify the occupation or task suitable for the individual. In this way, the collected data can be analyzed to identify the occupation or task suitable for the individual. 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 the collected data into a generation AI and have the generation AI analyze the data.

[0032] The suggestion unit can suggest a job type or task based on the analysis results. The suggestion unit can suggest a job type or task based on, for example, the results of an aptitude test. For example, the suggestion unit can suggest a job type or task based on the results of an aptitude test. The suggestion unit can also suggest a job type or task based on past success stories. For example, the suggestion unit can suggest a job type or task based on past success stories. Furthermore, the suggestion unit can use AI to suggest a job type or task that is optimal for an individual. For example, the suggestion unit can use AI to suggest a job type or task that is optimal for an individual. In this way, by suggesting a job type or task based on the analysis results, it is possible to make a suggestion that is optimal for an individual. Some or all of the above-mentioned processing in the suggestion unit can be performed using AI, for example, or without using AI. For example, the suggestion unit can input the analysis results to a generation AI and cause the generation AI to suggest a job type or task.

[0033] The placement unit can place personnel based on the proposed job types and tasks. The placement unit, for example, places personnel based on the needs of the department. For example, the placement unit places personnel based on the needs of the department. The placement unit can also place personnel based on individual preferences. For example, the placement unit places personnel based on individual preferences. Furthermore, the placement unit can perform optimal placement using AI. For example, the placement unit performs optimal placement using AI. As a result, by placing personnel based on the proposed job types and tasks, the productivity of the department can be improved. Some or all of the above-mentioned processing in the placement unit may be performed using AI, for example, or may be performed without using AI. For example, the placement unit can input the proposed job types and tasks into a generation AI and have the generation AI execute the placement of personnel.

[0034] The selection unit can select companies that meet the set criteria. The selection unit, for example, selects outsourcing partners based on the evaluation criteria of the companies. For example, the selection unit selects outsourcing partners based on the evaluation criteria of the companies. The selection unit can also select outsourcing partners based on the suitability of the business. For example, the selection unit selects outsourcing partners based on the suitability of the business. Furthermore, the selection unit can also select the optimal outsourcing partner using AI. For example, the selection unit selects the optimal outsourcing partner using AI. This allows for the selection of companies that meet the criteria, thereby improving business efficiency. Some or all of the above-mentioned processing in the selection unit may be performed using AI, or may be performed without using AI. For example, the selection unit can input the evaluation criteria of the companies into the generation AI and cause the generation AI to select the outsourcing partner.

[0035] The collection unit can analyze the user's past work history and select the optimal collection method. For example, if the user has past experience in project management, the collection unit asks detailed questions related to the project. For example, if the user has past experience in project management, the collection unit asks detailed questions related to the project. Furthermore, if the user has past experience in data analysis, the collection unit can also collect specific skills related to data analysis and experience using tools. For example, if the user has past experience in data analysis, the collection unit collects specific skills related to data analysis and experience using tools. Furthermore, if the user has past experience in sales, the collection unit can collect detailed information about sales performance and customer service experience. For example, if the user has past experience in sales, the collection unit collects detailed information about sales performance and customer service experience. This allows the optimal collection method to be selected by analyzing the user's past work 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 the user's past work history data into the generation AI and cause the generation AI to select a collection method.

[0036] When collecting skills, experience, and interests, the collection unit can filter the collected skills, experience, and interests based on the user's current project or area of ​​interest. For example, if the user is currently engaged in a data analysis project, the collection unit prioritizes collecting skills and experience related to data analysis. For example, if the user is currently engaged in a data analysis project, the collection unit prioritizes collecting skills and experience related to data analysis. Furthermore, if the user is currently interested in a marketing project, the collection unit can collect skills and experience related to marketing. For example, if the user is currently interested in a marketing project, the collection unit collects skills and experience related to marketing. Furthermore, if the user is currently interested in an IT project, the collection unit can collect skills and experience related to IT. For example, if the user is currently interested in an IT project, the collection unit collects skills and experience related to IT. This allows highly relevant information to be collected by filtering based on the user's current project or area of ​​interest. Some or all of the above-described processing by the collection unit may be performed using AI, for example, or without AI. For example, the collection unit can input data on the user's current project or area of ​​interest to the generation AI and have the generation AI perform the filtering.

[0037] When collecting skills, experience, and interests, the collection unit can prioritize collecting highly relevant information by taking into account the user's geographical location information. For example, if the user lives in an urban area, the collection unit prioritizes collecting work experience and skills in the urban area. For example, if the user lives in an urban area, the collection unit prioritizes collecting work experience and skills in the urban area. Furthermore, if the user lives in a rural area, the collection unit can prioritize collecting work experience and skills in the rural area. For example, if the user lives in a rural area, the collection unit prioritizes collecting work experience and skills in the rural area. Furthermore, if the user lives overseas, the collection unit can prioritize collecting work experience and skills overseas. For example, if the user lives overseas, the collection unit prioritizes collecting work experience and skills overseas. In this way, highly relevant information can be prioritized by taking the user's geographical location information into account. Some or all of the above-described processing by the collection unit may be performed using AI, for example, or may be performed without using AI. For example, the collection unit may input the user's geographical location information to the generation AI and cause the generation AI to collect information.

[0038] When collecting skills, experience, and interests, the collection unit can analyze the user's social media activities and collect related information. For example, the collection unit analyzes content that the user frequently posts on social media to collect interests. For example, the collection unit analyzes content that the user frequently posts on social media to collect interests. The collection unit can also analyze accounts that the user follows on social media to collect related skills and experience. For example, the collection unit analyzes accounts that the user follows on social media to collect related skills and experience. The collection unit can also analyze groups and communities that the user participates in on social media to collect related information. For example, the collection unit analyzes groups and communities that the user participates in on social media to collect related information. In this way, related information can be collected by analyzing the user's social media activities. Some or all of the above-mentioned processing by the collection unit may be performed using AI, for example, or may be performed without using AI. For example, the collection unit can input the user's social media activity data to a generation AI and cause the generation AI to collect information.

[0039] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of skills and experience. For example, the analysis unit performs a detailed analysis on important skills and experience. For example, the analysis unit performs a detailed analysis on important skills and experience. The analysis unit can also perform a concise analysis on general skills and experience. For example, the analysis unit performs a concise analysis on general skills and experience. Furthermore, the analysis unit can also perform a task-specific analysis on skills and experience related to a specific job. For example, the analysis unit performs a task-specific analysis on skills and experience related to a specific job. This enables efficient analysis by adjusting the level of detail of the analysis based on the importance of skills and experience. Some or all of the above-described processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input skill and experience data into a generation AI and cause the generation AI to adjust the level of detail of the analysis.

[0040] During analysis, the analysis unit can apply different analysis algorithms depending on the category of skills and experience. For example, the analysis unit applies a technical analysis algorithm to technical skills. For example, the analysis unit applies a technical analysis algorithm to technical skills. The analysis unit can also apply an analysis algorithm specialized for management to management skills. For example, the analysis unit applies an analysis algorithm specialized for management to management skills. The analysis unit can also apply a creative analysis algorithm to creative skills. For example, the analysis unit applies a creative analysis algorithm to creative skills. This enables more accurate analysis by applying different analysis algorithms depending on the category of skills and experience. 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 skill and experience category data into the generation AI and cause the generation AI to apply the analysis algorithm.

[0041] During analysis, the analysis unit can determine the priority of the analysis based on the time of submission of skills and experience. For example, the analysis unit prioritizes the analysis of recently submitted skills and experience. For example, the analysis unit prioritizes the analysis of recently submitted skills and experience. The analysis unit can also emphasize the latest information while referring to previously submitted skills and experience. For example, the analysis unit emphasizes the latest information while referring to previously submitted skills and experience. Furthermore, the analysis unit can adjust the level of detail of the analysis depending on the time of submission. For example, the analysis unit adjusts the level of detail of the analysis depending on the time of submission. This enables efficient analysis by determining the priority of the analysis based on the time of submission of skills and experience. 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 data on the time of submission of skills and experience to the generation AI and have the generation AI determine the priority of the analysis.

[0042] During analysis, the analysis unit can adjust the order of analysis based on the relevance of skills and experience. For example, the analysis unit prioritizes analysis of highly relevant skills and experience. For example, the analysis unit prioritizes analysis of highly relevant skills and experience. The analysis unit can also postpone analysis of less relevant skills and experience. For example, the analysis unit postpones analysis of less relevant skills and experience. Furthermore, the analysis unit can also adjust the level of detail of the analysis based on the relevance of skills and experience. For example, the analysis unit adjusts the level of detail of the analysis based on the relevance of skills and experience. This enables efficient analysis by adjusting the order of analysis based on the relevance of skills and experience. 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 relevance data of skills and experience to the generation AI and cause the generation AI to adjust the order of analysis.

[0043] When making a proposal, the suggestion unit can adjust the level of detail of the proposal based on the importance of the job type or task. For example, the suggestion unit makes detailed proposals for important job types or tasks. For example, the suggestion unit makes detailed proposals for important job types or tasks. The suggestion unit can also make concise proposals for general job types or tasks. For example, the suggestion unit makes concise proposals for general job types or tasks. The suggestion unit can also make task-specific proposals for job types or tasks related to specific tasks. For example, the suggestion unit makes task-specific proposals for job types or tasks related to specific tasks. This enables efficient proposals by adjusting the level of detail of the proposal based on the importance of the job type or task. Some or all of the above-mentioned processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input importance data of job types and tasks to the generation AI and cause the generation AI to adjust the level of detail of the proposal.

[0044] When making a proposal, the proposal unit can apply different proposal algorithms depending on the job type or job category. For example, the proposal unit applies a technical proposal algorithm to technical job types or jobs. For example, the proposal unit applies a technical proposal algorithm to technical job types or jobs. The proposal unit can also apply a proposal algorithm specialized for management to management job types or jobs. For example, the proposal unit applies a proposal algorithm specialized for management to management job types or jobs. The proposal unit can also apply a creative proposal algorithm to creative job types or jobs. For example, the proposal unit applies a creative proposal algorithm to creative job types or jobs. This enables more accurate proposals by applying different proposal algorithms depending on the job type or job category. Some or all of the above-mentioned processing in the proposal unit may be performed using AI, for example, or may be performed without using AI. For example, the proposal unit can input job type or job category data into a generation AI and cause the generation AI to apply a proposal algorithm.

[0045] When making a proposal, the suggestion unit can determine the priority of the proposal based on the submission date of the job type or task. For example, the suggestion unit prioritizes the most recently submitted job type or task. For example, the suggestion unit prioritizes the most recently submitted job type or task. The suggestion unit can also emphasize the latest information while referring to job types and tasks submitted in the past. For example, the suggestion unit emphasizes the latest information while referring to job types and tasks submitted in the past. Furthermore, the suggestion unit can adjust the level of detail of the proposal depending on the submission date. For example, the suggestion unit adjusts the level of detail of the proposal depending on the submission date. This enables efficient proposals by determining the priority of the proposal based on the submission date of the job type or task. Some or all of the above-described processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can input data on the submission date of the job type or task into the generation AI and cause the generation AI to determine the priority of the proposals.

[0046] When making a proposal, the suggestion unit can adjust the order of proposals based on the relevance of the job types and tasks. For example, the suggestion unit prioritizes proposing highly relevant job types and tasks. For example, the suggestion unit prioritizes proposing highly relevant job types and tasks. The suggestion unit can also postpone less relevant job types and tasks. For example, the suggestion unit postpones less relevant job types and tasks. Furthermore, the suggestion unit can also adjust the level of detail of the proposal depending on the relevance of the job types and tasks. For example, the suggestion unit adjusts the level of detail of the proposal depending on the relevance of the job types and tasks. This enables efficient proposals by adjusting the order of proposals based on the relevance of the job types and tasks. Some or all of the above-described processing in the suggestion unit may be performed using, or without, AI. For example, the suggestion unit can input relevance data of job types and tasks to a generation AI and cause the generation AI to adjust the order of proposals.

[0047] During placement, the placement unit can analyze the user's past placement history to select the optimal placement method. For example, the placement unit selects the optimal placement method by referring to placement methods that the user has used successfully in the past. Furthermore, if a specific placement method is found to be effective based on the user's past placement history, the placement unit can preferentially select that method. For example, if a specific placement method is found to be effective based on the user's past placement history, the placement unit can preferentially select that method. Furthermore, the placement unit can analyze the user's past placement history to select the most efficient placement method. For example, the placement unit analyzes the user's past placement history to select the most efficient placement method. In this way, the optimal placement method can be selected by analyzing the user's past placement history. Some or all of the above-described processing in the placement unit may be performed using, or without, AI. For example, the placement unit can input the user's past placement history data into a generation AI and have the generation AI select a placement method.

[0048] The placement unit can customize the placement method based on the user's current living situation during placement. For example, if the user desires remote work, the placement unit proposes a placement method that enables remote work. For example, if the user desires remote work, the placement unit proposes a placement method that enables remote work. Furthermore, if the user wants to shorten their commute time, the placement unit can also propose placement to a nearby office. For example, if the user wants to shorten their commute time, the placement unit can propose placement to a nearby office. Furthermore, if the user cannot work during a specific time period due to family circumstances, the placement unit can also propose a placement method that corresponds to that time period. For example, if the user cannot work during a specific time period due to family circumstances, the placement unit proposes a placement method that corresponds to that time period. This enables more appropriate placement by customizing the placement method based on the user's current living situation. Some or all of the above-described processing in the placement unit may be performed using AI, for example, or may be performed without AI. For example, the placement unit can input the user's living situation data into the generation AI and cause the generation AI to customize the placement method.

[0049] The placement unit can select the optimal placement method by taking into account the user's geographical location information during placement. For example, if the user lives in an urban area, the placement unit preferentially proposes a placement method for urban areas. For example, if the user lives in an urban area, the placement unit preferentially proposes a placement method for urban areas. Furthermore, if the user lives in a rural area, the placement unit can preferentially propose a placement method for rural areas. For example, if the user lives in a rural area, the placement unit preferentially proposes a placement method for rural areas. Furthermore, if the user lives overseas, the placement unit can preferentially propose a placement method for overseas locations. For example, if the user lives overseas, the placement unit preferentially proposes a placement method for overseas locations. In this way, the optimal placement method can be selected by taking into account the user's geographical location information. Some or all of the above-described processing in the placement unit may be performed using AI, for example, or may be performed without using AI. For example, the placement unit can input the user's geographical location information to the generation AI and cause the generation AI to select a placement method.

[0050] The placement unit can analyze the user's social media activity and suggest placement methods during placement. For example, the placement unit analyzes the content the user frequently posts on social media and suggests placement methods based on the user's interests. For example, the placement unit analyzes the content the user frequently posts on social media and suggests placement methods based on the user's interests. The placement unit can also analyze the accounts the user follows on social media and suggest related placement methods. For example, the placement unit analyzes the accounts the user follows on social media and suggests related placement methods. The placement unit can also analyze groups and communities the user participates in on social media and suggest related placement methods. For example, the placement unit analyzes the groups and communities the user participates in on social media and suggests related placement methods. In this way, by analyzing the user's social media activity, more appropriate placement methods can be suggested. Some or all of the above-described processing in the placement unit may be performed using, for example, AI, or may be performed without using AI. For example, the placement unit can input the user's social media activity data into a generation AI and cause the generation AI to suggest placement methods.

[0051] The selection unit can optimize the selection algorithm by referring to past selection data during selection. The selection unit, for example, applies an optimal selection algorithm based on selection data that has been successful in the past. For example, the selection unit applies an optimal selection algorithm based on selection data that has been successful in the past. Furthermore, if a specific selection criterion is found to be effective based on the past selection data, the selection unit can preferentially apply that criterion. For example, if a specific selection criterion is found to be effective based on the past selection data, the selection unit can preferentially apply that criterion. Furthermore, the selection unit can analyze the past selection data and apply the most efficient selection algorithm. For example, the selection unit analyzes the past selection data and applies the most efficient selection algorithm. In this way, the selection algorithm can be optimized by referring to the past selection data. Some or all of the above-described processing in the selection unit may be performed using, or without, AI. For example, the selection unit may input past selection data into a generation AI and cause the generation AI to optimize the selection algorithm.

[0052] The selection unit can make the selection taking into consideration the attribute information of the companies. The selection unit applies optimal selection criteria, for example, based on the industry and size of the companies. For example, the selection unit applies optimal selection criteria based on the industry and size of the companies. The selection unit can also apply optimal selection criteria based on the location and market share of the companies. For example, the selection unit applies optimal selection criteria based on the location and market share of the companies. The selection unit can also apply optimal selection criteria based on the financial situation and growth potential of the companies. For example, the selection unit applies optimal selection criteria based on the financial situation and growth potential of the companies. This enables more appropriate selection by taking the attribute information of the companies into consideration. Some or all of the above-mentioned processing in the selection unit can be performed using AI, for example, or without AI. For example, the selection unit can input the attribute information of the companies into the generation AI and have the generation AI perform the selection.

[0053] The selection unit can make the selection taking into consideration the geographic distribution of companies. The selection unit applies optimal selection criteria, for example, based on the locations of the companies. For example, the selection unit applies optimal selection criteria based on the locations of the companies. The selection unit can also apply optimal selection criteria based on the market share of the companies. For example, the selection unit applies optimal selection criteria based on the market share of the companies. The selection unit can also apply optimal selection criteria based on the geographic distribution of companies. For example, the selection unit applies optimal selection criteria based on the geographic distribution of companies. This enables more appropriate selection by taking the geographic distribution of companies into consideration. Some or all of the above-described processing in the selection unit may be performed using, for example, AI, or may be performed without using AI. For example, the selection unit can input geographic distribution data of companies into the generation AI and have the generation AI perform the selection.

[0054] The selection unit can improve the accuracy of the selection by referring to literature related to the company during selection. The selection unit can improve the accuracy of the selection by referring to, for example, a company's performance report. For example, the selection unit can improve the accuracy of the selection by referring to a company's performance report. The selection unit can also improve the accuracy of the selection by referring to a market analysis report of the company. For example, the selection unit can improve the accuracy of the selection by referring to a market analysis report of the company. Furthermore, the selection unit can improve the accuracy of the selection by referring to a financial report of the company. For example, the selection unit can improve the accuracy of the selection by referring to a financial report of the company. In this way, the accuracy of the selection can be improved by referring to literature related to the company. Some or all of the above-mentioned processing in the selection unit can be performed using, for example, AI, or can be performed without using AI. For example, the selection unit can input literature data related to the company into the generation AI and cause the generation AI to improve the accuracy of the selection.

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

[0056] The collection unit can analyze the success rate of the user's past projects and select the optimal collection method. For example, detailed questions can be asked based on data from projects the user has previously succeeded. It can also collect points for improvement based on data from projects the user has previously failed. It can also collect related skills and experience depending on the types of projects the user has previously been involved in. This allows the optimal collection method to be selected by analyzing the success rate of the user's past projects. 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 user's past project data into the generation AI and have the generation AI select the collection method.

[0057] When making a proposal, the suggestion unit can analyze the user's past feedback and customize the content of the proposal. For example, if the user has provided positive feedback in response to a proposal received in the past, the suggestion unit can make a new proposal by referring to the content of the proposal. Also, if the user has provided negative feedback in the past, the suggestion unit can make a proposal that reflects improvements based on that feedback. Furthermore, the suggestion unit can analyze the user's past feedback and select the most effective suggestion method. In this way, the content of the proposal can be customized by analyzing the user's past feedback. Some or all of the above-mentioned processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can input the user's past feedback data into a generation AI and have the generation AI customize the content of the proposal.

[0058] When making a selection, the selection unit can analyze the company's past performance data and apply the optimal selection criteria. For example, the selection criteria can be set based on data on projects that the company has previously succeeded. Risk can also be assessed based on data on projects that the company has previously failed. Furthermore, the selection unit can analyze the company's past performance data and select the most effective selection criteria. In this way, the optimal selection criteria can be applied by analyzing the company's past performance data. Some or all of the above-mentioned processing in the selection unit may be performed using, for example, AI, or may be performed without using AI. For example, the selection unit can input the company's past performance data into the generation AI and have the generation AI apply the selection criteria.

[0059] During analysis, the analysis unit can determine the analysis priorities taking into account the user's current work situation. For example, it can prioritize analysis of skills and experience related to a project the user is currently working on. It can also analyze skills and experience related to projects the user plans to work on in the future. Furthermore, it can adjust the level of detail of the analysis depending on the user's work situation. This enables efficient analysis by taking the user's current work situation into consideration. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, AI, or can be performed without using AI. For example, the analysis unit can input the user's work situation data into the generation AI and have the generation AI determine the analysis priorities.

[0060] When making a proposal, the proposal unit can analyze the user's past work history and select the optimal proposal method. For example, detailed proposals can be made based on data on tasks the user has previously succeeded in. Also, proposals that reflect areas for improvement can be made based on data on tasks the user has previously failed in. Furthermore, the proposal unit can analyze the user's past work history and select the most effective proposal method. In this way, the optimal proposal method can be selected by analyzing the user's past work history. Some or all of the above-mentioned processing in the proposal unit can be performed, for example, using AI, or can be performed without using AI. For example, the proposal unit can input the user's past work history data into the generation AI and have the generation AI select a proposal method.

[0061] The processing flow of the first embodiment will be briefly explained below.

[0062] Step 1: The collection department collects the skills, experience, and interests of individuals. Collection methods include questionnaires, interviews, and retrieval from databases. For example, an online questionnaire can be conducted to collect the skills, experience, and interests of individuals. Detailed information can also be collected through interviews. Furthermore, the skills, experience, and interests of individuals can be retrieved from databases. Step 2: The analysis unit analyzes the data collected by the collection unit and identifies the type of job or task suitable for the individual. Analysis methods include statistical analysis, machine learning algorithms, and data mining technology. For example, the collected data may be analyzed using statistical analysis to identify the type of job or task suitable for the individual. Alternatively, the data may be analyzed using machine learning algorithms to identify the type of job or task suitable for the individual. Furthermore, the data may be analyzed using data mining technology to identify the type of job or task suitable for the individual. Step 3: The proposal unit proposes occupations and tasks based on the analysis results obtained by the analysis unit. Proposal methods include proposals based on aptitude test results, proposals based on past success stories, and proposals using AI. For example, occupations and tasks can be proposed based on aptitude test results. It is also possible to propose occupations and tasks based on past success stories. Furthermore, it is possible to use AI to propose occupations and tasks that are best suited to individuals. Step 4: The placement department places personnel based on the job types and tasks proposed by the proposal department. Placement methods include placement based on department needs, individual preferences, and optimal placement using AI. For example, personnel can be placed based on department needs. They can also be placed based on individual preferences. Furthermore, optimal placement can be performed using AI. Step 5: The selection department selects a contractor based on the information on the personnel assigned by the placement department. Selection methods include selection based on the company's evaluation criteria and suitability for the business, and optimal selection using AI. For example, a contractor can be selected based on the company's evaluation criteria. It is also possible to select a contractor based on suitability for the business. Furthermore, it is also possible to use AI to select the optimal contractor.

[0063] (Example 2) A staffing system according to an embodiment of the present invention proposes job types and tasks suited to individuals and assigns appropriate personnel to necessary departments, thereby improving employee satisfaction (ES) and company productivity. This staffing system collects individuals' skills, experience, and interests, and uses AI to analyze these data. Based on the analysis results, the system then proposes the best job types and tasks for each individual. Furthermore, the system assigns appropriate personnel to necessary departments based on the proposed job types and tasks. This improves employee satisfaction and increases the company's overall productivity. Furthermore, when selecting outsourcing partners, it is possible to select companies that meet the desired criteria. For example, when collecting an individual's skills, experience, and interests, detailed information is collected about the individual's past work experience, skills, and interests, as well as their interests. For example, if an individual has experience in project management and is interested in data analysis, this information is collected. Next, AI analyzes the collected data. The AI ​​analyzes the individual's skills, experience, and interests to identify the best job types and tasks for each individual. For example, the system can propose data analysis project management to an individual with project management experience and an interest in data analysis. Based on the proposed job types and tasks, the system assigns appropriate personnel to necessary departments. For example, by assigning the right personnel to a department that needs to manage a data analysis project, the department's productivity can be improved. Furthermore, when selecting a contractor, it is possible to select a company that meets the desired criteria. For example, if personnel with specific skills or experience are needed, selecting a company that meets those criteria can improve work efficiency. This system improves employee satisfaction and increases the productivity of the entire company. For example, when individuals perform tasks that are suited to them, their motivation for work increases and work efficiency improves. Furthermore, by assigning the right personnel to the right department, the productivity of the entire department improves. Furthermore, when selecting a contractor, it is possible to improve work efficiency by selecting a company that meets the desired criteria. In this way, the personnel assignment system can improve employee satisfaction and increase the productivity of the entire company.

[0064] A staffing system according to an embodiment includes a collection unit, an analysis unit, a proposal unit, a placement unit, and a selection unit. The collection unit collects individual skills, experience, and interests. Examples of the individual skills, experience, and interests include, but are not limited to, technical skills, work experience, and hobbies. The collection unit, for example, collects the individual skills, experience, and interests using a questionnaire. The collection unit can also collect detailed information through interviews. The collection unit can also acquire the individual skills, experience, and interests from a database. For example, the collection unit conducts an online questionnaire to collect the individual skills, experience, and interests. The collection unit can also collect detailed information about the individual through interviews. The collection unit can also acquire the individual skills, experience, and interests from a database. The analysis unit analyzes the data collected by the collection unit and identifies a job type or task suitable for the individual. The analysis is performed using, for example, statistical analysis or a machine learning algorithm, but is not limited to, examples. For example, the analysis unit analyzes the collected data using statistical analysis to identify a job type or task suitable for the individual. The analysis unit can also analyze data using a machine learning algorithm to identify job types and tasks suitable for individuals. The analysis unit can also analyze data using data mining technology to identify job types and tasks suitable for individuals. The proposal unit proposes job types and tasks based on the analysis results obtained by the analysis unit. Proposals include, but are not limited to, proposals based on aptitude test results and proposals based on past success stories. For example, the proposal unit proposes job types and tasks based on aptitude test results. The proposal unit can also propose job types and tasks based on past success stories. The proposal unit can also use AI to propose job types and tasks optimal for individuals. The placement unit assigns personnel based on the job types and tasks proposed by the proposal unit. The placement is performed, for example, based on departmental needs or individual preferences, but is not limited to, examples. For example, the placement unit assigns personnel based on departmental needs. The placement unit can also assign personnel based on individual preferences. The placement unit can also perform optimal placement using AI. The selection unit selects a contractor based on information about the personnel assigned by the placement unit.The selection may be based on, for example, the company's evaluation criteria and suitability for the business, but is not limited to such examples. For example, the selection unit selects a business outsourcing partner based on the company's evaluation criteria. The selection unit can also select a business outsourcing partner based on suitability for the business. Furthermore, the selection unit can also select the optimal business outsourcing partner using AI. As a result, the staffing system according to the embodiment can improve employee satisfaction and improve the productivity of the entire company.

[0065] The collection unit can collect individual skills, experience, and interests. The collection unit, for example, collects individual skills, experience, and interests using a questionnaire. For example, the collection unit conducts an online questionnaire to collect individual skills, experience, and interests. The collection unit can also collect detailed information about individuals through interviews. For example, the collection unit collects individual skills, experience, and interests through interviews. The collection unit can also acquire individual skills, experience, and interests from a database. For example, the collection unit acquires individual skills, experience, and interests from a database. By collecting individual skills, experience, and interests in detail, more appropriate jobs and tasks can be suggested. Some or all of the above-described processing in the collection unit may be performed using, or without, AI. For example, the collection unit can input questionnaire response data into a generation AI and cause the generation AI to analyze the response data.

[0066] The analysis unit can analyze the collected data and identify the occupation or task suitable for the individual. The analysis unit can, for example, analyze the collected data using statistical analysis and identify the occupation or task suitable for the individual. For example, the analysis unit can analyze the collected data using statistical analysis and identify the occupation or task suitable for the individual. The analysis unit can also analyze the data using a machine learning algorithm and identify the occupation or task suitable for the individual. For example, the analysis unit can analyze the data using a machine learning algorithm and identify the occupation or task suitable for the individual. The analysis unit can also analyze the data using data mining technology and identify the occupation or task suitable for the individual. For example, the analysis unit can analyze the data using data mining technology and identify the occupation or task suitable for the individual. In this way, the collected data can be analyzed to identify the occupation or task suitable for the individual. 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 the collected data into a generation AI and have the generation AI analyze the data.

[0067] The suggestion unit can suggest a job type or task based on the analysis results. The suggestion unit can suggest a job type or task based on, for example, the results of an aptitude test. For example, the suggestion unit can suggest a job type or task based on the results of an aptitude test. The suggestion unit can also suggest a job type or task based on past success stories. For example, the suggestion unit can suggest a job type or task based on past success stories. Furthermore, the suggestion unit can use AI to suggest a job type or task that is optimal for an individual. For example, the suggestion unit can use AI to suggest a job type or task that is optimal for an individual. In this way, by suggesting a job type or task based on the analysis results, it is possible to make a suggestion that is optimal for an individual. Some or all of the above-mentioned processing in the suggestion unit can be performed using AI, for example, or without using AI. For example, the suggestion unit can input the analysis results to a generation AI and cause the generation AI to suggest a job type or task.

[0068] The placement unit can place personnel based on the proposed job types and tasks. The placement unit, for example, places personnel based on the needs of the department. For example, the placement unit places personnel based on the needs of the department. The placement unit can also place personnel based on individual preferences. For example, the placement unit places personnel based on individual preferences. Furthermore, the placement unit can perform optimal placement using AI. For example, the placement unit performs optimal placement using AI. As a result, by placing personnel based on the proposed job types and tasks, the productivity of the department can be improved. Some or all of the above-mentioned processing in the placement unit may be performed using AI, for example, or may be performed without using AI. For example, the placement unit can input the proposed job types and tasks into a generation AI and have the generation AI execute the placement of personnel.

[0069] The selection unit can select companies that meet the set criteria. The selection unit, for example, selects outsourcing partners based on the evaluation criteria of the companies. For example, the selection unit selects outsourcing partners based on the evaluation criteria of the companies. The selection unit can also select outsourcing partners based on the suitability of the business. For example, the selection unit selects outsourcing partners based on the suitability of the business. Furthermore, the selection unit can also select the optimal outsourcing partner using AI. For example, the selection unit selects the optimal outsourcing partner using AI. This allows for the selection of companies that meet the criteria, thereby improving business efficiency. Some or all of the above-mentioned processing in the selection unit may be performed using AI, or may be performed without using AI. For example, the selection unit can input the evaluation criteria of the companies into the generation AI and cause the generation AI to select the outsourcing partner.

[0070] The collection unit can estimate the user's emotions and adjust the timing of collecting skills, experience, and interests based on the estimated user emotions. For example, when the user is relaxed, the collection unit conducts a long interview to collect detailed skills, experience, and interests. For example, when the user is relaxed, the collection unit conducts a long interview to collect detailed skills, experience, and interests. Furthermore, when the user is feeling stressed, the collection unit can ask concise questions in a short time to collect the minimum amount of information necessary. For example, when the user is feeling stressed, the collection unit can ask concise questions in a short time to collect the minimum amount of information necessary. Furthermore, when the user is in a hurry, the collection unit can quickly collect information using voice input or multiple-choice questions. For example, when the user is in a hurry, the collection unit quickly collects information using voice input or multiple-choice questions. This enables more appropriate information collection by adjusting the collection timing according to the user's emotions. Emotion estimation is realized using an emotion estimation function using, 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 the user's emotion data into the generation AI and cause the generation AI to adjust the collection timing.

[0071] The collection unit can analyze the user's past work history and select the optimal collection method. For example, if the user has past experience in project management, the collection unit asks detailed questions related to the project. For example, if the user has past experience in project management, the collection unit asks detailed questions related to the project. Furthermore, if the user has past experience in data analysis, the collection unit can also collect specific skills related to data analysis and experience using tools. For example, if the user has past experience in data analysis, the collection unit collects specific skills related to data analysis and experience using tools. Furthermore, if the user has past experience in sales, the collection unit can collect detailed information about sales performance and customer service experience. For example, if the user has past experience in sales, the collection unit collects detailed information about sales performance and customer service experience. This allows the optimal collection method to be selected by analyzing the user's past work 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 the user's past work history data into the generation AI and cause the generation AI to select a collection method.

[0072] When collecting skills, experience, and interests, the collection unit can filter the collected skills, experience, and interests based on the user's current project or area of ​​interest. For example, if the user is currently engaged in a data analysis project, the collection unit prioritizes collecting skills and experience related to data analysis. For example, if the user is currently engaged in a data analysis project, the collection unit prioritizes collecting skills and experience related to data analysis. Furthermore, if the user is currently interested in a marketing project, the collection unit can collect skills and experience related to marketing. For example, if the user is currently interested in a marketing project, the collection unit collects skills and experience related to marketing. Furthermore, if the user is currently interested in an IT project, the collection unit can collect skills and experience related to IT. For example, if the user is currently interested in an IT project, the collection unit collects skills and experience related to IT. This allows highly relevant information to be collected by filtering based on the user's current project or area of ​​interest. Some or all of the above-described processing by the collection unit may be performed using AI, for example, or without AI. For example, the collection unit can input data on the user's current project or area of ​​interest to the generation AI and have the generation AI perform the filtering.

[0073] 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 relaxed, the collection unit prioritizes collecting detailed skills, experiences, and interests. For example, when the user is relaxed, the collection unit prioritizes collecting detailed skills, experiences, and interests. Furthermore, when the user is stressed, the collection unit can prioritize collecting basic skills, experiences, and interests. For example, when the user is stressed, the collection unit prioritizes collecting basic skills, experiences, and interests. Furthermore, when the user is in a hurry, the collection unit can prioritize collecting the most important skills, experiences, and interests. For example, when the user is in a hurry, the collection unit prioritizes collecting the most important skills, experiences, and interests. This enables efficient information collection by determining the priority of information to be collected according to the user's emotions. 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 collection unit may be performed using, for example, AI, or may be performed without using AI. For example, the collection unit may input user emotion data into the generation AI and have the generation AI determine the priority of the information.

[0074] When collecting skills, experience, and interests, the collection unit can prioritize collecting highly relevant information by taking into account the user's geographical location information. For example, if the user lives in an urban area, the collection unit prioritizes collecting work experience and skills in the urban area. For example, if the user lives in an urban area, the collection unit prioritizes collecting work experience and skills in the urban area. Furthermore, if the user lives in a rural area, the collection unit can prioritize collecting work experience and skills in the rural area. For example, if the user lives in a rural area, the collection unit prioritizes collecting work experience and skills in the rural area. Furthermore, if the user lives overseas, the collection unit can prioritize collecting work experience and skills overseas. For example, if the user lives overseas, the collection unit prioritizes collecting work experience and skills overseas. In this way, highly relevant information can be prioritized by taking the user's geographical location information into account. Some or all of the above-described processing by the collection unit may be performed using AI, for example, or may be performed without using AI. For example, the collection unit may input the user's geographical location information to the generation AI and cause the generation AI to collect information.

[0075] When collecting skills, experience, and interests, the collection unit can analyze the user's social media activities and collect related information. For example, the collection unit analyzes content that the user frequently posts on social media to collect interests. For example, the collection unit analyzes content that the user frequently posts on social media to collect interests. The collection unit can also analyze accounts that the user follows on social media to collect related skills and experience. For example, the collection unit analyzes accounts that the user follows on social media to collect related skills and experience. The collection unit can also analyze groups and communities that the user participates in on social media to collect related information. For example, the collection unit analyzes groups and communities that the user participates in on social media to collect related information. In this way, related information can be collected by analyzing the user's social media activities. Some or all of the above-mentioned processing by the collection unit may be performed using AI, for example, or may be performed without using AI. For example, the collection unit can input the user's social media activity data to a generation AI and cause the generation AI to collect information.

[0076] The analysis unit can estimate the user's emotions and adjust the way the analysis is presented based on the estimated user's emotions. For example, when the user is relaxed, the analysis unit provides detailed analysis results. Furthermore, when the user is stressed, the analysis unit can provide concise and to-the-point analysis results. For example, when the user is stressed, the analysis unit provides concise and to-the-point analysis results. Furthermore, when the user is in a hurry, the analysis unit can provide analysis results that focus on the most important points. This allows for adjusting the way the analysis is presented based on 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 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, an AI, or without an AI. For example, the analysis unit can input the user's emotional data into the generation AI and have the generation AI adjust the way the analysis is expressed.

[0077] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of skills and experience. For example, the analysis unit performs a detailed analysis on important skills and experience. For example, the analysis unit performs a detailed analysis on important skills and experience. The analysis unit can also perform a concise analysis on general skills and experience. For example, the analysis unit performs a concise analysis on general skills and experience. Furthermore, the analysis unit can also perform a task-specific analysis on skills and experience related to a specific job. For example, the analysis unit performs a task-specific analysis on skills and experience related to a specific job. This enables efficient analysis by adjusting the level of detail of the analysis based on the importance of skills and experience. Some or all of the above-described processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input skill and experience data into a generation AI and cause the generation AI to adjust the level of detail of the analysis.

[0078] During analysis, the analysis unit can apply different analysis algorithms depending on the category of skills and experience. For example, the analysis unit applies a technical analysis algorithm to technical skills. For example, the analysis unit applies a technical analysis algorithm to technical skills. The analysis unit can also apply an analysis algorithm specialized for management to management skills. For example, the analysis unit applies an analysis algorithm specialized for management to management skills. The analysis unit can also apply a creative analysis algorithm to creative skills. For example, the analysis unit applies a creative analysis algorithm to creative skills. This enables more accurate analysis by applying different analysis algorithms depending on the category of skills and experience. 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 skill and experience category data into the generation AI and cause the generation AI to apply the analysis algorithm.

[0079] The analysis unit can estimate the user's emotions and adjust the length of the analysis based on the estimated user's emotions. For example, when the user is relaxed, the analysis unit provides detailed analysis results. For example, when the user is relaxed, the analysis unit provides detailed analysis results. Furthermore, when the user is stressed, the analysis unit can provide concise and to-the-point analysis results. For example, when the user is stressed, the analysis unit provides concise and to-the-point analysis results. Furthermore, when the user is in a hurry, the analysis unit can provide analysis results that focus on the most important points. For example, when the user is in a hurry, the analysis unit provides analysis results that focus on the most important points. This allows for adjusting the length of the analysis according to the user's emotions to provide more appropriate analysis results. 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 analysis unit can be performed using, for example, AI, or without AI. For example, the analysis unit can input user emotion data into the generation AI and have the generation AI adjust the length of the analysis.

[0080] During analysis, the analysis unit can determine the priority of the analysis based on the time of submission of skills and experience. For example, the analysis unit prioritizes the analysis of recently submitted skills and experience. For example, the analysis unit prioritizes the analysis of recently submitted skills and experience. The analysis unit can also emphasize the latest information while referring to previously submitted skills and experience. For example, the analysis unit emphasizes the latest information while referring to previously submitted skills and experience. Furthermore, the analysis unit can adjust the level of detail of the analysis depending on the time of submission. For example, the analysis unit adjusts the level of detail of the analysis depending on the time of submission. This enables efficient analysis by determining the priority of the analysis based on the time of submission of skills and experience. 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 data on the time of submission of skills and experience to the generation AI and have the generation AI determine the priority of the analysis.

[0081] During analysis, the analysis unit can adjust the order of analysis based on the relevance of skills and experience. For example, the analysis unit prioritizes analysis of highly relevant skills and experience. For example, the analysis unit prioritizes analysis of highly relevant skills and experience. The analysis unit can also postpone analysis of less relevant skills and experience. For example, the analysis unit postpones analysis of less relevant skills and experience. Furthermore, the analysis unit can also adjust the level of detail of the analysis based on the relevance of skills and experience. For example, the analysis unit adjusts the level of detail of the analysis based on the relevance of skills and experience. This enables efficient analysis by adjusting the order of analysis based on the relevance of skills and experience. 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 relevance data of skills and experience to the generation AI and cause the generation AI to adjust the order of analysis.

[0082] The suggestion unit can estimate the user's emotions and adjust the way suggestions are expressed based on the estimated user emotions. For example, when the user is relaxed, the suggestion unit provides detailed suggestions. For example, when the user is relaxed, the suggestion unit provides detailed suggestions. Furthermore, when the user is stressed, the suggestion unit can provide concise and to-the-point suggestions. For example, when the user is stressed, the suggestion unit provides concise and to-the-point suggestions. Furthermore, when the user is in a hurry, the suggestion unit can provide suggestions that focus on the most important points. For example, when the user is in a hurry, the suggestion unit provides suggestions that focus on the most important points. This enables more appropriate suggestions by adjusting the way suggestions are expressed based on 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 a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-described processing in the suggestion unit can be performed using, for example, an AI, or without an AI. For example, the suggestion unit can input the user's emotional data into the generation AI and have the generation AI adjust the way the suggestion is expressed.

[0083] When making a proposal, the suggestion unit can adjust the level of detail of the proposal based on the importance of the job type or task. For example, the suggestion unit makes detailed proposals for important job types or tasks. For example, the suggestion unit makes detailed proposals for important job types or tasks. The suggestion unit can also make concise proposals for general job types or tasks. For example, the suggestion unit makes concise proposals for general job types or tasks. The suggestion unit can also make task-specific proposals for job types or tasks related to specific tasks. For example, the suggestion unit makes task-specific proposals for job types or tasks related to specific tasks. This enables efficient proposals by adjusting the level of detail of the proposal based on the importance of the job type or task. Some or all of the above-mentioned processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input importance data of job types and tasks to the generation AI and cause the generation AI to adjust the level of detail of the proposal.

[0084] When making a proposal, the proposal unit can apply different proposal algorithms depending on the job type or job category. For example, the proposal unit applies a technical proposal algorithm to technical job types or jobs. For example, the proposal unit applies a technical proposal algorithm to technical job types or jobs. The proposal unit can also apply a proposal algorithm specialized for management to management job types or jobs. For example, the proposal unit applies a proposal algorithm specialized for management to management job types or jobs. The proposal unit can also apply a creative proposal algorithm to creative job types or jobs. For example, the proposal unit applies a creative proposal algorithm to creative job types or jobs. This enables more accurate proposals by applying different proposal algorithms depending on the job type or job category. Some or all of the above-mentioned processing in the proposal unit may be performed using AI, for example, or may be performed without using AI. For example, the proposal unit can input job type or job category data into a generation AI and cause the generation AI to apply a proposal algorithm.

[0085] The suggestion unit can estimate the user's emotions and adjust the length of the suggestions based on the estimated user's emotions. For example, when the user is relaxed, the suggestion unit provides detailed suggestions. For example, when the user is relaxed, the suggestion unit provides detailed suggestions. Furthermore, when the user is stressed, the suggestion unit can provide concise and to-the-point suggestions. For example, when the user is stressed, the suggestion unit provides concise and to-the-point suggestions. Furthermore, when the user is in a hurry, the suggestion unit can provide suggestions that focus on the most important points. For example, when the user is in a hurry, the suggestion unit provides suggestions that focus on the most important points. This enables more appropriate suggestions by adjusting the length of the suggestions according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-mentioned processing in the suggestion unit can be performed, for example, using AI or without AI. For example, the suggestion unit can input the user's emotional data into the generation AI and cause the generation AI to adjust the length of the suggestion.

[0086] When making a proposal, the suggestion unit can determine the priority of the proposal based on the submission date of the job type or task. For example, the suggestion unit prioritizes the most recently submitted job type or task. For example, the suggestion unit prioritizes the most recently submitted job type or task. The suggestion unit can also emphasize the latest information while referring to job types and tasks submitted in the past. For example, the suggestion unit emphasizes the latest information while referring to job types and tasks submitted in the past. Furthermore, the suggestion unit can adjust the level of detail of the proposal depending on the submission date. For example, the suggestion unit adjusts the level of detail of the proposal depending on the submission date. This enables efficient proposals by determining the priority of the proposal based on the submission date of the job type or task. Some or all of the above-described processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can input data on the submission date of the job type or task into the generation AI and cause the generation AI to determine the priority of the proposals.

[0087] When making a proposal, the suggestion unit can adjust the order of proposals based on the relevance of the job types and tasks. For example, the suggestion unit prioritizes proposing highly relevant job types and tasks. For example, the suggestion unit prioritizes proposing highly relevant job types and tasks. The suggestion unit can also postpone less relevant job types and tasks. For example, the suggestion unit postpones less relevant job types and tasks. Furthermore, the suggestion unit can also adjust the level of detail of the proposal depending on the relevance of the job types and tasks. For example, the suggestion unit adjusts the level of detail of the proposal depending on the relevance of the job types and tasks. This enables efficient proposals by adjusting the order of proposals based on the relevance of the job types and tasks. Some or all of the above-described processing in the suggestion unit may be performed using, or without, AI. For example, the suggestion unit can input relevance data of job types and tasks to a generation AI and cause the generation AI to adjust the order of proposals.

[0088] The placement unit can estimate the user's emotions and adjust the placement method based on the estimated user's emotions. For example, when the user is relaxed, the placement unit provides a detailed placement method. For example, when the user is relaxed, the placement unit provides a detailed placement method. Furthermore, when the user is stressed, the placement unit can provide a concise and to-the-point placement method. For example, when the user is stressed, the placement unit provides a concise and to-the-point placement method. Furthermore, when the user is in a hurry, the placement unit can provide a placement method that focuses on the most important points. For example, when the user is in a hurry, the placement unit provides a placement method that focuses on the most important points. This allows for more appropriate placement by adjusting the placement method 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 a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the placement unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the placement unit can input the user's emotion data into the generation AI and cause the generation AI to adjust the placement method.

[0089] During placement, the placement unit can analyze the user's past placement history to select the optimal placement method. For example, the placement unit selects the optimal placement method by referring to placement methods that the user has used successfully in the past. Furthermore, if a specific placement method is found to be effective based on the user's past placement history, the placement unit can preferentially select that method. For example, if a specific placement method is found to be effective based on the user's past placement history, the placement unit can preferentially select that method. Furthermore, the placement unit can analyze the user's past placement history to select the most efficient placement method. For example, the placement unit analyzes the user's past placement history to select the most efficient placement method. In this way, the optimal placement method can be selected by analyzing the user's past placement history. Some or all of the above-described processing in the placement unit may be performed using, or without, AI. For example, the placement unit can input the user's past placement history data into a generation AI and have the generation AI select a placement method.

[0090] The placement unit can customize the placement method based on the user's current living situation during placement. For example, if the user desires remote work, the placement unit proposes a placement method that enables remote work. For example, if the user desires remote work, the placement unit proposes a placement method that enables remote work. Furthermore, if the user wants to shorten their commute time, the placement unit can also propose placement to a nearby office. For example, if the user wants to shorten their commute time, the placement unit can propose placement to a nearby office. Furthermore, if the user cannot work during a specific time period due to family circumstances, the placement unit can also propose a placement method that corresponds to that time period. For example, if the user cannot work during a specific time period due to family circumstances, the placement unit proposes a placement method that corresponds to that time period. This enables more appropriate placement by customizing the placement method based on the user's current living situation. Some or all of the above-described processing in the placement unit may be performed using AI, for example, or may be performed without AI. For example, the placement unit can input the user's living situation data into the generation AI and cause the generation AI to customize the placement method.

[0091] The placement unit can estimate the user's emotions and determine placement priorities based on the estimated user emotions. For example, when the user is relaxed, the placement unit provides a detailed placement method. For example, when the user is relaxed, the placement unit provides a detailed placement method. Furthermore, when the user is stressed, the placement unit can provide a concise and to-the-point placement method. For example, when the user is stressed, the placement unit provides a concise and to-the-point placement method. Furthermore, when the user is in a hurry, the placement unit can provide a placement method that focuses on the most important points. For example, when the user is in a hurry, the placement unit provides a placement method that focuses on the most important points. This enables more appropriate placement by determining placement priorities according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the placement unit can be performed, for example, using AI or without AI. For example, the placement unit can input the user's emotion data into the generation AI and have the generation AI determine placement priorities.

[0092] The placement unit can select the optimal placement method by taking into account the user's geographical location information during placement. For example, if the user lives in an urban area, the placement unit preferentially proposes a placement method for urban areas. For example, if the user lives in an urban area, the placement unit preferentially proposes a placement method for urban areas. Furthermore, if the user lives in a rural area, the placement unit can preferentially propose a placement method for rural areas. For example, if the user lives in a rural area, the placement unit preferentially proposes a placement method for rural areas. Furthermore, if the user lives overseas, the placement unit can preferentially propose a placement method for overseas locations. For example, if the user lives overseas, the placement unit preferentially proposes a placement method for overseas locations. In this way, the optimal placement method can be selected by taking into account the user's geographical location information. Some or all of the above-described processing in the placement unit may be performed using AI, for example, or may be performed without using AI. For example, the placement unit can input the user's geographical location information to the generation AI and cause the generation AI to select a placement method.

[0093] The placement unit can analyze the user's social media activity and suggest placement methods during placement. For example, the placement unit analyzes the content the user frequently posts on social media and suggests placement methods based on the user's interests. For example, the placement unit analyzes the content the user frequently posts on social media and suggests placement methods based on the user's interests. The placement unit can also analyze the accounts the user follows on social media and suggest related placement methods. For example, the placement unit analyzes the accounts the user follows on social media and suggests related placement methods. The placement unit can also analyze groups and communities the user participates in on social media and suggest related placement methods. For example, the placement unit analyzes the groups and communities the user participates in on social media and suggests related placement methods. In this way, by analyzing the user's social media activity, more appropriate placement methods can be suggested. Some or all of the above-described processing in the placement unit may be performed using, for example, AI, or may be performed without using AI. For example, the placement unit can input the user's social media activity data into a generation AI and cause the generation AI to suggest placement methods.

[0094] The selection unit can estimate the user's emotions and adjust the selection criteria based on the estimated user's emotions. For example, when the user is relaxed, the selection unit provides detailed selection criteria. For example, when the user is relaxed, the selection unit provides detailed selection criteria. Furthermore, when the user is stressed, the selection unit can provide concise and to-the-point selection criteria. For example, when the user is stressed, the selection unit provides concise and to-the-point selection criteria. Furthermore, when the user is in a hurry, the selection unit can provide selection criteria that focus on the most important points. For example, when the user is in a hurry, the selection unit provides selection criteria that focus on the most important points. This enables more appropriate selection by adjusting the selection criteria according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-mentioned processing in the selection unit can be performed, for example, using AI or without AI. For example, the selection unit can input the user's emotion data into the generation AI and cause the generation AI to adjust the selection criteria.

[0095] The selection unit can optimize the selection algorithm by referring to past selection data during selection. The selection unit, for example, applies an optimal selection algorithm based on selection data that has been successful in the past. For example, the selection unit applies an optimal selection algorithm based on selection data that has been successful in the past. Furthermore, if a specific selection criterion is found to be effective based on the past selection data, the selection unit can preferentially apply that criterion. For example, if a specific selection criterion is found to be effective based on the past selection data, the selection unit can preferentially apply that criterion. Furthermore, the selection unit can analyze the past selection data and apply the most efficient selection algorithm. For example, the selection unit analyzes the past selection data and applies the most efficient selection algorithm. In this way, the selection algorithm can be optimized by referring to the past selection data. Some or all of the above-described processing in the selection unit may be performed using, or without, AI. For example, the selection unit may input past selection data into a generation AI and cause the generation AI to optimize the selection algorithm.

[0096] The selection unit can make the selection taking into consideration the attribute information of the companies. The selection unit applies optimal selection criteria, for example, based on the industry and size of the companies. For example, the selection unit applies optimal selection criteria based on the industry and size of the companies. The selection unit can also apply optimal selection criteria based on the location and market share of the companies. For example, the selection unit applies optimal selection criteria based on the location and market share of the companies. The selection unit can also apply optimal selection criteria based on the financial situation and growth potential of the companies. For example, the selection unit applies optimal selection criteria based on the financial situation and growth potential of the companies. This enables more appropriate selection by taking the attribute information of the companies into consideration. Some or all of the above-mentioned processing in the selection unit can be performed using AI, for example, or without AI. For example, the selection unit can input the attribute information of the companies into the generation AI and have the generation AI perform the selection.

[0097] The selection unit can estimate the user's emotions and adjust the order in which the selection results are displayed based on the estimated user's emotions. For example, when the user is relaxed, the selection unit provides detailed selection results. For example, when the user is relaxed, the selection unit provides detailed selection results. Furthermore, when the user is stressed, the selection unit can provide concise and to-the-point selection results. For example, when the user is stressed, the selection unit provides concise and to-the-point selection results. Furthermore, when the user is in a hurry, the selection unit can provide selection results that focus on the most important points. For example, when the user is in a hurry, the selection unit provides selection results that focus on the most important points. In this way, by adjusting the order in which the selection results are displayed according to the user's emotions, more appropriate selection results can be provided. 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 selection unit can be performed using, for example, an AI, or without using an AI. For example, the selection unit can input the user's emotion data into the generation AI and cause the generation AI to adjust the display order of the selection results.

[0098] The selection unit can make the selection taking into consideration the geographic distribution of companies. The selection unit applies optimal selection criteria, for example, based on the locations of the companies. For example, the selection unit applies optimal selection criteria based on the locations of the companies. The selection unit can also apply optimal selection criteria based on the market share of the companies. For example, the selection unit applies optimal selection criteria based on the market share of the companies. The selection unit can also apply optimal selection criteria based on the geographic distribution of companies. For example, the selection unit applies optimal selection criteria based on the geographic distribution of companies. This enables more appropriate selection by taking the geographic distribution of companies into consideration. Some or all of the above-described processing in the selection unit may be performed using, for example, AI, or may be performed without using AI. For example, the selection unit can input geographic distribution data of companies into the generation AI and have the generation AI perform the selection.

[0099] The selection unit can improve the accuracy of the selection by referring to literature related to the company during selection. The selection unit can improve the accuracy of the selection by referring to, for example, a company's performance report. For example, the selection unit can improve the accuracy of the selection by referring to a company's performance report. The selection unit can also improve the accuracy of the selection by referring to a market analysis report of the company. For example, the selection unit can improve the accuracy of the selection by referring to a market analysis report of the company. Furthermore, the selection unit can improve the accuracy of the selection by referring to a financial report of the company. For example, the selection unit can improve the accuracy of the selection by referring to a financial report of the company. In this way, the accuracy of the selection can be improved by referring to literature related to the company. Some or all of the above-mentioned processing in the selection unit can be performed using, for example, AI, or can be performed without using AI. For example, the selection unit can input literature data related to the company into the generation AI and cause the generation AI to improve the accuracy of the selection. === Hard Collateral 1-1 === Each of the multiple elements, including the collection unit, analysis unit, proposal unit, allocation unit, and selection 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 collects an individual's skills, experience, and interests using the reception device 38 and camera 42 of the smart device 14. The analysis unit analyzes the data collected by, for example, the identification processing unit 290 of the data processing device 12 and identifies a job type or task suitable for the individual. The proposal unit proposes a job type or task based on the analysis results by, for example, the identification processing unit 290 of the data processing device 12. The allocation unit allocates personnel based on the job type or task proposed by, for example, the control unit 46A of the smart device 14. The selection unit selects a contractor based on information about the personnel allocated by, for example, the identification processing unit 290 of the data processing device 12. === Hard Collateral 1-2 === Each of the multiple elements, including the collection unit, analysis unit, proposal unit, allocation unit, and selection 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 collects an individual's skills, experience, and interests using the microphone 238 and camera 42 of the smart glasses 214. The analysis unit analyzes the data collected, for example, by the identification processing unit 290 of the data processing device 12, and identifies a job type or task suitable for the individual. The proposal unit proposes a job type or task based on the analysis results, for example, by the identification processing unit 290 of the data processing device 12. The allocation unit allocates personnel based on the job type or task proposed, for example, by the control unit 46A of the smart glasses 214. The selection unit selects a contractor based on information about the personnel allocated, for example, by the identification processing unit 290 of the data processing device 12. === Hard Collateral 1-3 === Each of the multiple elements including the collection unit, analysis unit, proposal unit, placement unit, and selection 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 collects an individual's skills, experience, and interests using the microphone 238 and camera 42 of the headset-type terminal 314. The analysis unit analyzes the data collected by, for example, the identification processing unit 290 of the data processing device 12 and identifies a job type or task suitable for the individual. The proposal unit proposes a job type or task based on the analysis results by, for example, the identification processing unit 290 of the data processing device 12. The placement unit assigns personnel based on the job type or task proposed by, for example, the control unit 46A of the headset-type terminal 314. The selection unit selects a contractor based on information about the personnel assigned by, for example, the identification processing unit 290 of the data processing device 12. === Hard Collateral 1-4 === Each of the multiple elements including the collection unit, analysis unit, proposal unit, placement unit, and selection 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 collects individual skills, experience, and interests using the microphone 238 and camera 42 of the robot 414. The analysis unit analyzes data collected, for example, by the identification processing unit 290 of the data processing device 12, and identifies occupations and tasks suitable for the individual. The proposal unit proposes occupations and tasks based on the analysis results, for example, by the identification processing unit 290 of the data processing device 12. The placement unit assigns personnel based on occupations and tasks proposed, for example, by the control unit 46A of the robot 414. The selection unit selects a contractor based on information about personnel assigned by the identification processing unit 290 of the data processing device 12.

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

[0101] The suggestion unit can estimate the user's emotions and adjust the timing of suggestions based on the estimated user emotions. For example, if the user is relaxed, a long explanation can be provided to provide detailed suggestions. Furthermore, if the user is stressed, a concise suggestion can be provided in a short time, providing the minimum necessary information. Furthermore, if the user is in a hurry, a suggestion focused on the most important points can be quickly provided. This allows for more appropriate suggestions by adjusting the timing of suggestions 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 may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the suggestion unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the suggestion unit can input the user's emotion data into the generation AI and cause the generation AI to adjust the timing of suggestions.

[0102] The collection unit can analyze the success rate of the user's past projects and select the optimal collection method. For example, detailed questions can be asked based on data from projects the user has previously succeeded. It can also collect points for improvement based on data from projects the user has previously failed. It can also collect related skills and experience depending on the types of projects the user has previously been involved in. This allows the optimal collection method to be selected by analyzing the success rate of the user's past projects. 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 user's past project data into the generation AI and have the generation AI select the collection method.

[0103] The analysis unit can estimate the user's emotions and adjust the analysis visualization method based on the estimated user emotions. For example, if the user is relaxed, detailed graphs and charts can be used to visualize the analysis results. If the user is stressed, a concise and to-the-point visualization method can be used. If the user is in a hurry, a visualization method that emphasizes the most important points can be used. By adjusting the analysis visualization method according to the user's emotions, more appropriate analysis results can be provided. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the analysis unit can be performed using AI, or without AI. For example, the analysis unit can input the user's emotion data into the generation AI and have the generation AI adjust the visualization method.

[0104] When making a proposal, the suggestion unit can analyze the user's past feedback and customize the content of the proposal. For example, if the user has provided positive feedback in response to a proposal received in the past, the suggestion unit can make a new proposal by referring to the content of the proposal. Also, if the user has provided negative feedback in the past, the suggestion unit can make a proposal that reflects improvements based on that feedback. Furthermore, the suggestion unit can analyze the user's past feedback and select the most effective suggestion method. In this way, the content of the proposal can be customized by analyzing the user's past feedback. Some or all of the above-mentioned processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can input the user's past feedback data into a generation AI and have the generation AI customize the content of the proposal.

[0105] The placement unit can estimate the user's emotions and adjust the placement notification method based on the estimated user's emotions. For example, if the user is relaxed, email or text can be used to provide a detailed placement notification. If the user is stressed, a short message can be used to provide a concise placement notification. If the user is in a hurry, a phone call or chat can be used to provide a placement notification that emphasizes the most important points. This allows for more appropriate placement notifications by adjusting the placement notification 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 a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the placement unit can be performed using AI, or without AI. For example, the placement unit can input the user's emotion data into the generation AI and have the generation AI adjust the notification method.

[0106] When making a selection, the selection unit can analyze the company's past performance data and apply the optimal selection criteria. For example, the selection criteria can be set based on data on projects that the company has previously succeeded. Risk can also be assessed based on data on projects that the company has previously failed. Furthermore, the selection unit can analyze the company's past performance data and select the most effective selection criteria. In this way, the optimal selection criteria can be applied by analyzing the company's past performance data. Some or all of the above-mentioned processing in the selection unit may be performed using, for example, AI, or may be performed without using AI. For example, the selection unit can input the company's past performance data into the generation AI and have the generation AI apply the selection criteria.

[0107] The collection unit can estimate the user's emotions and adjust the format of the information to be collected based on the estimated user emotions. For example, if the user is relaxed, detailed text-format information can be collected. If the user is stressed, concise checklist-format information can be collected. If the user is in a hurry, voice input or multiple-choice information can be collected. This allows for more appropriate information collection by adjusting the format of the information to be collected 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 can be performed using, for example, AI, or without AI. For example, the collection unit can input the user's emotion data into the generation AI and have the generation AI adjust the information format.

[0108] During analysis, the analysis unit can determine the analysis priorities taking into account the user's current work situation. For example, it can prioritize analysis of skills and experience related to a project the user is currently working on. It can also analyze skills and experience related to projects the user plans to work on in the future. Furthermore, it can adjust the level of detail of the analysis depending on the user's work situation. This enables efficient analysis by taking the user's current work situation into consideration. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, AI, or can be performed without using AI. For example, the analysis unit can input the user's work situation data into the generation AI and have the generation AI determine the analysis priorities.

[0109] When making a proposal, the proposal unit can analyze the user's past work history and select the optimal proposal method. For example, detailed proposals can be made based on data on tasks the user has previously succeeded in. Also, proposals that reflect areas for improvement can be made based on data on tasks the user has previously failed in. Furthermore, the proposal unit can analyze the user's past work history and select the most effective proposal method. In this way, the optimal proposal method can be selected by analyzing the user's past work history. Some or all of the above-mentioned processing in the proposal unit can be performed, for example, using AI, or can be performed without using AI. For example, the proposal unit can input the user's past work history data into the generation AI and have the generation AI select a proposal method.

[0110] The placement unit can estimate the user's emotions and adjust the placement feedback method based on the estimated user emotions. For example, if the user is relaxed, email or text can be used to provide detailed feedback. If the user is stressed, a short message can be used to provide concise feedback. If the user is in a hurry, a phone call or chat can be used to provide feedback emphasizing the most important points. This allows for more appropriate feedback by adjusting the placement feedback method 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 a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the placement unit can be performed using AI, for example, or without AI. For example, the placement unit can input the user's emotion data into the generation AI and have the generation AI adjust the feedback method.

[0111] The processing flow of the second embodiment will be briefly explained below.

[0112] Step 1: The collection department collects the skills, experience, and interests of individuals. Collection methods include questionnaires, interviews, and retrieval from databases. For example, an online questionnaire can be conducted to collect the skills, experience, and interests of individuals. Detailed information can also be collected through interviews. Furthermore, the skills, experience, and interests of individuals can be retrieved from databases. Step 2: The analysis unit analyzes the data collected by the collection unit and identifies the type of job or task suitable for the individual. Analysis methods include statistical analysis, machine learning algorithms, and data mining technology. For example, the collected data may be analyzed using statistical analysis to identify the type of job or task suitable for the individual. Alternatively, the data may be analyzed using machine learning algorithms to identify the type of job or task suitable for the individual. Furthermore, the data may be analyzed using data mining technology to identify the type of job or task suitable for the individual. Step 3: The proposal unit proposes occupations and tasks based on the analysis results obtained by the analysis unit. Proposal methods include proposals based on aptitude test results, proposals based on past success stories, and proposals using AI. For example, occupations and tasks can be proposed based on aptitude test results. It is also possible to propose occupations and tasks based on past success stories. Furthermore, it is possible to use AI to propose occupations and tasks that are best suited to individuals. Step 4: The placement department places personnel based on the job types and tasks proposed by the proposal department. Placement methods include placement based on department needs, individual preferences, and optimal placement using AI. For example, personnel can be placed based on department needs. They can also be placed based on individual preferences. Furthermore, optimal placement can be performed using AI. Step 5: The selection department selects a contractor based on the information on the personnel assigned by the placement department. Selection methods include selection based on the company's evaluation criteria and suitability for the business, and optimal selection using AI. For example, a contractor can be selected based on the company's evaluation criteria. It is also possible to select a contractor based on suitability for the business. Furthermore, it is also possible to use AI to select the optimal contractor.

[0113] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating 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.

[0114] 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.

[0115] 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.

[0116] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

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

[0118] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0119] The data processing device 12 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.

[0120] 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.

[0121] 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.

[0122] 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).

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating 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.

[0130] 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.

[0131] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is 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.

[0132] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

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

[0134] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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).

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is 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.

[0148] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

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

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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).

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the 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.

[0163] 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.

[0164] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is 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.

[0165] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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).

[0170] 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 indicated, and when they approach the ideal, a state of pleasure is indicated. 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 indicated, and when they approach the ideal, a state of pleasure is indicated. 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.

[0171] 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."

[0172] 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.

[0173] 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.

[0174] 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.

[0175] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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, to avoid confusion and 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.

[0183] 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.

[0184] [Explanation of symbols]

[0185] 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 individual skills, experiences, and interests; an analysis unit that analyzes the data collected by the collection unit; a proposal unit that proposes job types and tasks based on the analysis results obtained by the analysis unit; a placement department that assigns personnel based on the proposed job types and tasks; A selection unit that selects a business partner based on information about the deployed personnel. A system characterized by:

2. The collecting unit Collecting personal skills, experiences, and interests The system of claim 1 .

3. The analysis unit Analyze the collected data to identify suitable jobs and tasks for individuals The system of claim 1 .

4. The proposal unit Propose job types and tasks based on analysis results The system of claim 1 .

5. The placement unit Staffing based on proposed jobs and tasks The system of claim 1 .

6. The selection unit Select companies that meet the set criteria The system of claim 1 .

7. The collecting unit Estimate user emotions and adjust the timing of collecting skills, experiences, and interests based on the estimated user emotions. The system of claim 1 .

8. The collecting unit Analyze the user's past work history and select the appropriate collection method The system of claim 1 .

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