System

Abstract

An object of the system according to the embodiment is to evaluate hidden skills and personality characteristics of individuals and propose a career development plan based on the evaluation.SOLUTION: A system includes an evaluation part, an analysis part, a proposal part, and a company use part. The evaluator evaluates a skill or personality trait of the user. The analysis unit analyzes the evaluation result obtained by the evaluation unit. The proposing section proposes a career development plan based on the analysis result obtained by the analyzing section. A company use part allows the company to formulate an employee arrangement or raising plan on the basis of the plan proposed by the proposal part.SELECTED DRAWING: Figure 1

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 have had the problem of not being able to effectively evaluate an individual's hidden skills and personality traits and fully utilize this information for career development and corporate personnel placement.

[0005] The system according to the embodiment aims to evaluate an individual's hidden skills and personality traits and propose a career development plan based on the evaluation. [Means for solving the problem]

[0006] The system according to the embodiment includes an evaluation unit, an analysis unit, a proposal unit, and a corporate utilization unit. The evaluation unit evaluates the skills or personality characteristics of a user. The analysis unit analyzes the evaluation results obtained by the evaluation unit. The proposal unit proposes a career development plan based on the analysis results obtained by the analysis unit. The corporate utilization unit allows a company to formulate an employee placement or training plan based on the plan proposed by the proposal unit. [Effects of the Invention]

[0007] The system according to the embodiment can evaluate an individual's hidden skills and personality traits and propose a career development plan based on the evaluation. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) An AI-driven platform according to an embodiment of the present invention is a system that identifies individuals' hidden skills and personality traits and utilizes this knowledge for career development and self-growth. This system meets the needs of both individuals seeking to improve their self-awareness and companies seeking to maximize their employees' potential. For example, a user accesses the platform and takes a self-assessment test. The test assesses the user's skills and personality traits through various questions and scenarios. For example, problem-solving ability, communication skills, and leadership traits are assessed. The test results are analyzed by AI to reveal the user's hidden skills and traits. Next, the AI ​​proposes an optimal career development plan for the user based on the analysis results. For example, if the user has leadership traits, the AI ​​suggests leadership training or project management training. Furthermore, if the user wants to improve their communication skills, the AI ​​suggests workshops and courses to improve communication skills. Furthermore, companies can use this platform to maximize the potential of their employees. Companies can have their employees take self-assessment tests and use the results to understand their skills and traits. This allows them to formulate placement and development plans that leverage their strengths. For example, when a company launches a new project, it can use this platform to select the best personnel for the project. AI analyzes employees' skills and characteristics and suggests the best personnel for the project. This improves the project's success rate and increases the company's productivity. In this way, the AI-driven platform not only improves individuals' self-awareness and supports career development and personal growth, but also optimizes the company's utilization of human resources. By utilizing AI, it is possible to reveal users' hidden skills and characteristics and, based on that, suggest optimal career development plans and personnel placements.

[0029] An AI-driven platform according to an embodiment includes an evaluation unit, an analysis unit, a proposal unit, and a corporate utilization unit. The evaluation unit evaluates a user's skills or personality characteristics. Examples of the user's skills include, but are not limited to, technical skills, communication skills, and leadership skills. Examples of the personality characteristics include, but are not limited to, extroversion, agreeableness, and conscientiousness. The evaluation unit may conduct the evaluation using methods such as questionnaires, interviews, and tests. The evaluation unit may also estimate the user's emotions using AI and adjust the order of evaluation items based on the estimated user emotions. The analysis unit analyzes the evaluation results obtained by the evaluation unit. The analysis may be performed using methods such as, but are not limited to, statistical analysis, data mining, and machine learning. The analysis unit may, for example, refer to the user's past evaluation results and establish a feedback loop to improve the accuracy of the evaluation. The proposal unit proposes a career development plan based on the analysis results obtained by the analysis unit. Examples of the career development plan include, but are not limited to, training programs, promotion plans, and skill improvement plans. The suggestion unit can, for example, estimate a user's emotions and adjust the way the suggestion is expressed based on the estimated user's emotions. The corporate utilization unit allows a company to formulate an employee placement or development plan based on the plan proposed by the suggestion unit. The corporate utilization unit allows a company to formulate an employee placement or development plan using methods such as reassignment, implementation of a training program, or mentoring. As a result, the AI-driven platform according to the embodiment evaluates and analyzes a user's skills and personality characteristics, proposes a career development plan, and allows a company to formulate an employee placement or development plan.

[0030] The AI-driven platform includes a reception unit that accepts a user's self-assessment test. The reception unit accepts the user's self-assessment test. Examples of self-assessment tests include, but are not limited to, personality diagnostic tests and skill assessment tests. The reception unit accepts the self-assessment test, for example, through an online form. The reception unit can also estimate the user's emotions using AI and adjust the reception method based on the estimated user emotions. For example, if the user is nervous, a simple and intuitive reception method is provided. If the user is relaxed, a reception method including detailed information is provided. If the user is in a hurry, a quick and concise reception method is provided. This allows the user's self-assessment test to be accepted.

[0031] The AI-driven platform includes an evaluation item setting unit that sets evaluation items. The evaluation items include, but are not limited to, technical skills, communication skills, and leadership. The evaluation item setting unit sets the evaluation items using methods such as survey results, expert opinions, and past data analysis. The evaluation item setting unit can also estimate the user's emotions using AI and adjust the method for setting the evaluation items based on the estimated user's emotions. For example, if the user is nervous, simple and intuitive evaluation items are set. If the user is relaxed, evaluation items including detailed information are set. If the user is in a hurry, quick and concise evaluation items are set. In this way, the evaluation items can be set.

[0032] The AI-driven platform includes a storage unit that stores the evaluation results. The storage unit stores the evaluation results. Methods for storing the evaluation results include, but are not limited to, storing them in a database or cloud storage. The storage unit stores the evaluation results in a database, for example. The storage unit can also estimate the user's emotions using AI and determine the priority of stored data based on the estimated user's emotions. For example, if the user is nervous, important data is stored first. If the user is relaxed, detailed data is stored first. If the user is in a hurry, data that needs to be stored quickly is stored first. In this way, the evaluation results can be stored.

[0033] The AI-driven platform includes a corporate interface unit for use by companies. The corporate interface unit provides an interface for use by companies. Examples of interfaces include, but are not limited to, a web interface and a mobile application. The corporate interface unit provides, for example, a dashboard for companies to view and analyze employee evaluation results. The corporate interface unit can also use AI to estimate a user's emotions and adjust the interface display method based on the estimated user's emotions. For example, if the user is nervous, a simple, highly visible interface is provided. If the user is relaxed, an interface containing detailed information is provided. If the user is in a hurry, an interface that focuses on the main points is provided. In this way, interfaces for use by companies can be provided.

[0034] The AI-driven platform includes an analysis method section that describes in detail the algorithms or analysis methods used by the AI. The analysis method section specifically describes the algorithms and analysis methods used by the AI. Examples of algorithms and analysis methods include, but are not limited to, neural networks, support vector machines, and decision trees. The analysis method section, for example, uses a neural network to analyze a user's skills and personality characteristics. The analysis method section can also use AI to estimate the user's emotions and adjust the analysis method based on the estimated user emotions. For example, if the user is nervous, a simple and intuitive analysis method is applied. If the user is relaxed, an analysis method containing detailed information is applied. If the user is in a hurry, a quick and concise analysis method is applied. This allows the algorithms and analysis methods used by the AI ​​to be specifically described.

[0035] The evaluation unit can refer to the user's past evaluation results and build a feedback loop to improve the accuracy of the evaluation. For example, the evaluation unit adjusts the weighting of evaluation items based on the user's past evaluation results. The evaluation unit can also add questions about specific skills based on the user's past evaluation results. The evaluation unit can also analyze the user's past evaluation results and improve the algorithm for improving the accuracy of the evaluation. In this way, the accuracy of the evaluation can be improved by referring to the user's past evaluation results.

[0036] During the evaluation, the evaluation unit can customize the evaluation items based on the user's current job or life situation. For example, the evaluation unit adds questions for the user to evaluate skills related to their current job. The evaluation unit can also add questions related to stress management or time management depending on the user's life situation. The evaluation unit can also adjust the order of the evaluation items based on the user's job or life situation. This allows the evaluation items to be customized based on the user's current job or life situation.

[0037] The evaluation unit can select an appropriate evaluation means depending on the user's input method during evaluation. For example, if the user selects voice input, the evaluation unit performs evaluation using voice recognition technology. If the user selects text input, the evaluation unit can also perform evaluation using text analysis technology. If the user selects image input, the evaluation unit can also perform evaluation using image analysis technology. This makes it possible to select the optimal evaluation means depending on the user's input method.

[0038] During evaluation, the evaluation unit can prioritize presenting highly relevant evaluation items by taking into account the user's geographical location information. For example, if the user is in a specific region, the evaluation unit can add questions that evaluate skills and knowledge related to that region. The evaluation unit can also add questions that evaluate problem-solving abilities specific to the region based on the user's geographical location information. The evaluation unit can also adjust the order of evaluation items by taking into account the user's geographical location information. This makes it possible to prioritize presenting highly relevant evaluation items by taking into account the user's geographical location information.

[0039] During the evaluation, the evaluation unit can analyze the user's social media activity and add related evaluation items. For example, the evaluation unit can analyze the content of the user's social media posts and add questions about related skills. The evaluation unit can also add evaluation items based on the user's interests and concerns from the user's social media activity. The evaluation unit can also add related evaluation items by referring to the activities of the user's friends on social media. In this way, the user's social media activity can be analyzed and related evaluation items can be added.

[0040] The evaluation unit can customize the evaluation method by reflecting the user's past feedback during evaluation. For example, the evaluation unit adjusts the weighting of evaluation items based on feedback provided by the user in the past. The evaluation unit can also add questions about specific skills based on the user's past feedback. The evaluation unit can also analyze the user's past feedback and improve the evaluation method. This makes it possible to customize the evaluation method by reflecting the user's past feedback.

[0041] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the evaluation results. For example, the analysis unit can perform a detailed analysis of important evaluation results to provide deep insight. For less important evaluation results, the analysis unit can also perform a concise analysis to provide results that focus on the main points. The analysis unit can also determine the priority of the analysis based on the importance of the evaluation results. This makes it possible to adjust the level of detail of the analysis based on the importance of the evaluation results.

[0042] During analysis, the analysis unit can apply different analysis algorithms depending on the category of the evaluation item. For example, the analysis unit applies a specific analysis algorithm to evaluation items related to problem-solving ability. The analysis unit can also apply a different analysis algorithm to evaluation items related to communication skills. The analysis unit can also apply yet another different analysis algorithm to evaluation items related to leadership characteristics. In this way, different analysis algorithms can be applied depending on the category of the evaluation item.

[0043] During analysis, the analysis unit can improve the accuracy of the analysis by referring to the user's past analysis results. For example, the analysis unit improves the analysis algorithm based on the user's past analysis results. The analysis unit can also strengthen the analysis of specific skills based on the user's past analysis results. The analysis unit can also analyze the user's past analysis results and build a feedback loop to improve the accuracy of the analysis. This makes it possible to improve the accuracy of the analysis by referring to the user's past analysis results.

[0044] During analysis, the analysis unit can determine the priority of analysis based on the submission time of the evaluation results. For example, the analysis unit prioritizes the analysis of the most recently submitted evaluation results. The analysis unit can also postpone the analysis of evaluation results that were submitted earlier. The analysis unit can also adjust the analysis schedule based on the submission time. This makes it possible to determine the priority of analysis based on the submission time of the evaluation results.

[0045] The analysis unit can adjust the order of analysis based on the relevance of the evaluation results during analysis. For example, the analysis unit prioritizes analysis of highly relevant evaluation results. The analysis unit can also postpone analysis of less relevant evaluation results. The analysis unit can also adjust the order of analysis based on the relevance of the evaluation results. This makes it possible to adjust the order of analysis based on the relevance of the evaluation results.

[0046] During analysis, the analysis unit can adjust the use of technical terms in the analysis according to the user's level of expertise. For example, if the user has technical expertise, the analysis unit can provide analysis results that make heavy use of technical terms. If the user does not have technical expertise, the analysis unit can also provide analysis results that are concise and easy to understand. The analysis unit can also adjust the way in which the analysis results are expressed based on the user's level of expertise. This makes it possible to adjust the use of technical terms in the analysis according to the user's level of expertise.

[0047] When making a proposal, the proposal unit can adjust the level of detail of the proposal based on the importance of the career development plan. For example, the proposal unit makes a detailed proposal for an important career development plan. The proposal unit can also make a concise proposal for a less important career development plan. The proposal unit can also determine the priority of the proposal based on the importance of the career development plan. This makes it possible to adjust the level of detail of the proposal based on the importance of the career development plan.

[0048] When making a proposal, the suggestion unit can apply different suggestion algorithms depending on the category of the career development plan. For example, the suggestion unit applies a specific suggestion algorithm to a career development plan related to leadership development. The suggestion unit can also apply a different suggestion algorithm to a career development plan related to improving communication skills. The suggestion unit can also apply an even different suggestion algorithm to a career development plan related to improving problem-solving abilities. In this way, different suggestion algorithms can be applied depending on the category of the career development plan.

[0049] When making a suggestion, the suggestion unit can improve the accuracy of the suggestion by referring to the user's past suggestion results. For example, the suggestion unit improves the suggestion algorithm based on the user's past suggestion results. The suggestion unit can also strengthen suggestions regarding specific skills based on the user's past suggestion results. The suggestion unit can also analyze the user's past suggestion results and build a feedback loop to improve the accuracy of the suggestion. This makes it possible to improve the accuracy of the suggestion by referring to the user's past suggestion results.

[0050] When making a proposal, the proposal unit can determine the priority of the proposal based on the submission date of the career development plan. For example, the proposal unit gives priority to the most recently submitted career development plan. The proposal unit can also postpone the proposal of an older submitted career development plan. The proposal unit can also adjust the proposal schedule based on the submission date. This makes it possible to determine the priority of the proposal based on the submission date of the career development plan.

[0051] The proposal unit can adjust the order of proposals based on the relevance of the career development plans when making proposals. For example, the proposal unit preferentially proposes highly relevant career development plans. The proposal unit can also postpone proposing less relevant career development plans. The proposal unit can also adjust the order of proposals based on the relevance of the career development plans. This makes it possible to adjust the order of proposals based on the relevance of the career development plans.

[0052] When making a suggestion, the suggestion unit can adjust the use of technical terminology in the suggestion according to the user's level of expertise. For example, if the user has technical expertise, the suggestion unit can make a suggestion that uses a lot of technical terminology. If the user does not have technical expertise, the suggestion unit can also make a concise and easy-to-understand suggestion. The suggestion unit can also adjust the way the suggestion is expressed based on the user's level of expertise. This makes it possible to adjust the use of technical terminology in the suggestion according to the user's level of expertise.

[0053] When using the system for corporate use, the Corporate Use Department can analyze the employee's past evaluation results and select the optimal usage method. For example, the Corporate Use Department can propose the optimal usage method based on the employee's past evaluation results. The Corporate Use Department can also strengthen the usage method for specific skills based on the employee's past evaluation results. The Corporate Use Department can also analyze the employee's past evaluation results and improve the usage method. In this way, the optimal usage method can be selected by analyzing the employee's past evaluation results.

[0054] During corporate usage, the corporate usage department can customize the usage means based on the company's current projects and needs. For example, the corporate usage department provides usage means for assessing skills related to the company's current projects. The corporate usage department can also enhance usage means related to specific skills according to the company's needs. The corporate usage department can also adjust the order of usage means based on the company's projects and needs. This allows the usage means to be customized based on the company's current projects and needs.

[0055] The Corporate Utilization Department can improve the utilization method by reflecting the feedback from the company when the system is utilized by the company. For example, the Corporate Utilization Department adjusts the weighting of utilization methods based on the feedback from the company. The Corporate Utilization Department can also add utilization methods related to specific skills based on the feedback from the company. The Corporate Utilization Department can also analyze the feedback from the company and improve the utilization method. In this way, the utilization method can be improved by reflecting the feedback from the company.

[0056] The corporate utilization department can select the optimal utilization method in consideration of the geographical location information of the company when utilizing the company. For example, the corporate utilization department provides utilization methods that evaluate skills and knowledge specific to a region based on the geographical location information of the company. The corporate utilization department can also adjust the order of utilization methods in consideration of the geographical location information of the company. The corporate utilization department can also propose the optimal utilization method based on the geographical location information of the company. This makes it possible to select the optimal utilization method in consideration of the geographical location information of the company.

[0057] The Corporate Use Department can analyze a company's social media activity and suggest ways to use it when the company is using it. For example, the Corporate Use Department can analyze the content of a company's posts on social media and suggest ways to use it related to related skills. The Corporate Use Department can also suggest ways to use it based on interests and concerns from the company's social media activity. The Corporate Use Department can also suggest related ways to use it based on the company's social media activity. In this way, it is possible to analyze a company's social media activity and suggest ways to use it.

[0058] The Corporate Use Department can customize the usage method by reflecting the company's past feedback when the company uses the service. For example, the Corporate Use Department adjusts the weighting of usage methods based on the company's past feedback. The Corporate Use Department can also add usage methods related to specific skills based on the company's past feedback. The Corporate Use Department can also analyze the company's past feedback and improve the usage method. This makes it possible to customize the usage method by reflecting the company's past feedback.

[0059] The reception unit can select the optimal reception method by referring to the user's past evaluation results when receiving a call. The reception unit, for example, proposes the optimal reception method based on the user's past evaluation results. The reception unit can also strengthen the reception method for a specific skill based on the user's past evaluation results. The reception unit can also analyze the user's past evaluation results and improve the reception method. In this way, the optimal reception method can be selected by referring to the user's past evaluation results.

[0060] The reception unit can customize the reception items based on the user's current living situation and areas of interest at the time of reception. For example, the reception unit adds reception items related to stress management and time management according to the user's living situation. The reception unit can also add related reception items based on the user's areas of interest. The reception unit can also adjust the order of the reception items based on the user's living situation and areas of interest. This makes it possible to customize the reception items based on the user's current living situation and areas of interest.

[0061] The reception unit can prioritize presenting highly relevant reception items in consideration of the user's geographical location information when receiving a request. For example, the reception unit provides reception items that evaluate region-specific skills and knowledge based on the user's geographical location information. The reception unit can also adjust the order of the reception items in consideration of the user's geographical location information. The reception unit can also suggest optimal reception items based on the user's geographical location information. This makes it possible to prioritize presenting highly relevant reception items in consideration of the user's geographical location information.

[0062] The reception unit can analyze the user's social media activity and add related reception items when receiving a request. For example, the reception unit can analyze the content of the user's social media posts and add reception items related to related skills. The reception unit can also add reception items based on the user's interests and concerns from the user's social media activity. The reception unit can also add related reception items by referring to the activities of the user's friends on social media. In this way, the user's social media activity can be analyzed and related reception items can be added.

[0063] When setting evaluation items, the evaluation item setting unit can set optimal evaluation items by referring to the user's past evaluation results. The evaluation item setting unit sets optimal evaluation items, for example, based on the user's past evaluation results. The evaluation item setting unit can also add evaluation items related to specific skills from the user's past evaluation results. The evaluation item setting unit can also analyze the user's past evaluation results and improve the evaluation items. In this way, optimal evaluation items can be set by referring to the user's past evaluation results.

[0064] When setting the evaluation items, the evaluation item setting unit can customize the evaluation items based on the user's current job and lifestyle. The evaluation item setting unit sets, for example, evaluation items for evaluating skills related to the user's job. The evaluation item setting unit can also set evaluation items related to stress management and time management according to the user's lifestyle. The evaluation item setting unit can also adjust the order of the evaluation items based on the user's job and lifestyle. This makes it possible to customize the evaluation items based on the user's current job and lifestyle.

[0065] When setting evaluation items, the evaluation item setting unit can prioritize highly relevant evaluation items by taking into account the user's geographical location information. The evaluation item setting unit sets evaluation items that evaluate region-specific skills and knowledge, for example, based on the user's geographical location information. The evaluation item setting unit can also adjust the order of evaluation items by taking into account the user's geographical location information. The evaluation item setting unit can also suggest optimal evaluation items based on the user's geographical location information. This makes it possible to prioritize highly relevant evaluation items by taking into account the user's geographical location information.

[0066] When setting evaluation items, the evaluation item setting unit can analyze the user's social media activities and add related evaluation items. For example, the evaluation item setting unit analyzes the content of the user's posts on social media and adds evaluation items related to related skills. The evaluation item setting unit can also add evaluation items based on the user's interests and concerns from the user's social media activities. The evaluation item setting unit can also add related evaluation items by referring to the activities of the user's friends on social media. In this way, the user's social media activities can be analyzed and related evaluation items can be added.

[0067] The storage unit can adjust the level of detail of the storage based on the importance of the evaluation result when storing the evaluation result. For example, the storage unit stores detailed data for important evaluation results. The storage unit can also store concise data for less important evaluation results. The storage unit can also determine the priority of storage based on the importance of the evaluation result. This allows the level of detail of storage to be adjusted based on the importance of the evaluation result.

[0068] The storage unit may apply different storage algorithms depending on the category of the evaluation results when storing the results. For example, the storage unit may apply a specific storage algorithm to the evaluation results related to problem-solving ability. The storage unit may also apply a different storage algorithm to the evaluation results related to communication skills. The storage unit may also apply a further different storage algorithm to the evaluation results related to leadership traits. In this way, different storage algorithms may be applied depending on the category of the evaluation results.

[0069] The storage unit can determine the priority of storage based on the submission time of the evaluation results when storing the results. For example, the storage unit prioritizes storing the most recently submitted evaluation results. The storage unit can also store older submitted evaluation results later. The storage unit can also adjust the storage schedule based on the submission time. This makes it possible to determine the priority of storage based on the submission time of the evaluation results.

[0070] The storage unit can adjust the order of storage based on the relevance of the evaluation results when storing them. For example, the storage unit prioritizes storing highly relevant evaluation results. The storage unit can also store less relevant evaluation results later. The storage unit can also adjust the order of storage based on the relevance of the evaluation results. This makes it possible to adjust the order of storage based on the relevance of the evaluation results.

[0071] When displaying the interface, the corporate interface unit can select the optimal display method by referring to the company's past operation history. For example, the corporate interface unit proposes the optimal interface display method based on the company's past operation history. The corporate interface unit can also enhance the display method for a specific function based on the company's past operation history. The corporate interface unit can also analyze the company's past operation history and improve the interface display method. This makes it possible to select the optimal display method by referring to the company's past operation history.

[0072] The enterprise interface unit can customize the display content according to the enterprise's current tasks when displaying the interface. For example, the enterprise interface unit prioritizes displaying information related to the enterprise's current tasks. The enterprise interface unit can also customize the display content of the interface according to the enterprise's tasks. The enterprise interface unit can also adjust the display order of the interface based on the enterprise's tasks. This allows the display content to be customized according to the enterprise's current tasks.

[0073] The corporate interface unit can select the optimal display method when displaying an interface, taking into account the device information of the company. The corporate interface unit provides a display method that matches the screen size of the device used by the company, for example. The corporate interface unit can also propose the optimal interface display method based on the device information of the company. The corporate interface unit can also adjust the display order of the interface, taking into account the device information of the company. This makes it possible to select the optimal display method, taking into account the device information of the company.

[0074] The corporate interface unit can make the display content multilingual when displaying the interface according to the language setting of the company. For example, the corporate interface unit automatically sets the display language of the interface based on the language setting of the company. The corporate interface unit can also provide a language switching function when the company uses multiple languages. If the company selects a specific language, the corporate interface unit can also display the interface in that language. This makes it possible to make the display content multilingual according to the language setting of the company.

[0075] When setting an analysis method, the analysis method unit can adjust the level of detail of the analysis method based on the importance of the evaluation results. For example, the analysis method unit applies a detailed analysis method to important evaluation results. The analysis method unit can also apply a simple analysis method to evaluation results with low importance. The analysis method unit can also determine the priority of the analysis methods based on the importance of the evaluation results. This makes it possible to adjust the level of detail of the analysis method based on the importance of the evaluation results.

[0076] When setting the analysis method, the analysis method unit can apply different analysis methods depending on the category of the evaluation item. For example, the analysis method unit applies a specific analysis method to evaluation items related to problem-solving ability. The analysis method unit can also apply a different analysis method to evaluation items related to communication skills. The analysis method unit can also apply an even different analysis method to evaluation items related to leadership characteristics. In this way, different analysis methods can be applied depending on the category of the evaluation item.

[0077] When setting the analysis methods, the analysis method unit can determine the priority of the analysis methods based on the submission time of the evaluation results. For example, the analysis method unit prioritizes the analysis of the most recently submitted evaluation results. The analysis method unit can also postpone the analysis of evaluation results that were submitted earlier. The analysis method unit can also adjust the schedule of the analysis methods based on the submission time. This makes it possible to determine the priority of the analysis methods based on the submission time of the evaluation results.

[0078] When setting the analysis methods, the analysis method unit can adjust the order of the analysis methods based on the relevance of the evaluation results. For example, the analysis method unit prioritizes analysis of highly relevant evaluation results. The analysis method unit can also postpone analysis of less relevant evaluation results. The analysis method unit can also adjust the order of the analysis methods based on the relevance of the evaluation results. This makes it possible to adjust the order of the analysis methods based on the relevance of the evaluation results.

[0079] When setting an analysis method, the analysis method unit can adjust the use of technical terms in the analysis method according to the user's level of expertise. The analysis method unit adjusts the use of technical terms in the analysis method according to, for example, the user's level of expertise. If the user has technical knowledge, the analysis method unit can also apply an analysis method that makes heavy use of technical terms. If the user does not have technical knowledge, the analysis method unit can also apply a simple and easy-to-understand analysis method. This makes it possible to adjust the use of technical terms in the analysis method according to the user's level of expertise.

[0080] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0081] The analysis unit can create a feedback loop to improve the accuracy of the evaluation based on the user's past evaluation results. For example, it can strengthen questions about skills for which the user has previously received high ratings and perform a more detailed analysis. It can also clearly indicate areas for improvement and provide specific advice for skills for which the user has previously received low ratings. Furthermore, by tracking the user's evaluation results over time and analyzing skill growth and changes, it can propose more accurate career development plans.

[0082] The reception unit can refer to the user's past evaluation results and select the optimal reception method. For example, if the user has been nervous in the past, a simple and intuitive reception method can be provided. Also, if the user has been relaxed in the past, a reception method including detailed information can be provided. Furthermore, if the user has been in a hurry in the past, a quick and simple reception method can be provided. In this way, the optimal reception method can be selected based on the user's past evaluation results.

[0083] The evaluation item setting unit can customize the evaluation items based on the user's current job and life situation. For example, it can add questions to evaluate skills related to the user's current job. It can also add questions related to stress management and time management depending on the user's life situation. It can also adjust the order of the evaluation items based on the user's job and life situation. This allows the evaluation items to be customized based on the user's current job and life situation.

[0084] The storage unit can adjust the level of detail of storage based on the importance of the evaluation results. For example, detailed data can be stored for important evaluation results. Also, brief data can be stored for evaluation results with low importance. Furthermore, the priority of storage can be determined based on the importance of the evaluation results. In this way, the level of detail of storage can be adjusted based on the importance of the evaluation results.

[0085] The enterprise interface unit can customize the utilization means based on the current projects and needs of the enterprise. For example, it can provide utilization means for assessing skills related to the current projects of the enterprise. It can also enhance utilization means related to specific skills according to the needs of the enterprise. It can also adjust the order of utilization means based on the projects and needs of the enterprise. This allows the utilization means to be customized based on the current projects and needs of the enterprise.

[0086] The processing flow of the first embodiment will be briefly explained below.

[0087] Step 1: The evaluation unit evaluates the user's skills or personality traits. User skills include technical skills, communication skills, leadership skills, etc., while personality traits include extroversion, cooperativeness, conscientiousness, etc. The evaluation unit can use methods such as questionnaires, interviews, and tests to conduct the evaluation, and can also use AI to estimate the user's emotions and adjust the order of the evaluation items. Step 2: The analysis unit analyzes the evaluation results obtained by the evaluation unit. The analysis is performed using methods such as statistical analysis, data mining, and machine learning, and can refer to the user's past evaluation results and create a feedback loop to improve the accuracy of the evaluation. Step 3: The proposal unit proposes a career development plan based on the analysis results obtained by the analysis unit. The career development plan may include training programs, promotion plans, skill improvement plans, etc. The proposal unit can estimate the user's emotions and adjust the way the proposal is presented. Step 4: The Corporate Utilization Department formulates employee placement or development plans based on the plans proposed by the Proposal Department. The Corporate Utilization Department can help companies formulate employee placement or development plans using methods such as transfers, training programs, and mentoring.

[0088] (Example 2) An AI-driven platform according to an embodiment of the present invention is a system that identifies individuals' hidden skills and personality traits and utilizes this knowledge for career development and self-growth. This system meets the needs of both individuals seeking to improve their self-awareness and companies seeking to maximize their employees' potential. For example, a user accesses the platform and takes a self-assessment test. The test assesses the user's skills and personality traits through various questions and scenarios. For example, problem-solving ability, communication skills, and leadership traits are assessed. The test results are analyzed by AI to reveal the user's hidden skills and traits. Next, the AI ​​proposes an optimal career development plan for the user based on the analysis results. For example, if the user has leadership traits, the AI ​​suggests leadership training or project management training. Furthermore, if the user wants to improve their communication skills, the AI ​​suggests workshops and courses to improve communication skills. Furthermore, companies can use this platform to maximize the potential of their employees. Companies can have their employees take self-assessment tests and use the results to understand their skills and traits. This allows them to formulate placement and development plans that leverage their strengths. For example, when a company launches a new project, it can use this platform to select the best personnel for the project. AI analyzes employees' skills and characteristics and suggests the best personnel for the project. This improves the project's success rate and increases the company's productivity. In this way, the AI-driven platform not only improves individuals' self-awareness and supports career development and personal growth, but also optimizes the company's utilization of human resources. By utilizing AI, it is possible to reveal users' hidden skills and characteristics and, based on that, suggest optimal career development plans and personnel placements.

[0089] An AI-driven platform according to an embodiment includes an evaluation unit, an analysis unit, a proposal unit, and a corporate utilization unit. The evaluation unit evaluates a user's skills or personality characteristics. Examples of the user's skills include, but are not limited to, technical skills, communication skills, and leadership skills. Examples of the personality characteristics include, but are not limited to, extroversion, agreeableness, and conscientiousness. The evaluation unit may conduct the evaluation using methods such as questionnaires, interviews, and tests. The evaluation unit may also estimate the user's emotions using AI and adjust the order of evaluation items based on the estimated user emotions. The analysis unit analyzes the evaluation results obtained by the evaluation unit. The analysis may be performed using methods such as, but are not limited to, statistical analysis, data mining, and machine learning. The analysis unit may, for example, refer to the user's past evaluation results and establish a feedback loop to improve the accuracy of the evaluation. The proposal unit proposes a career development plan based on the analysis results obtained by the analysis unit. Examples of the career development plan include, but are not limited to, training programs, promotion plans, and skill improvement plans. The suggestion unit can, for example, estimate a user's emotions and adjust the way the suggestion is expressed based on the estimated user's emotions. The corporate utilization unit allows a company to formulate an employee placement or development plan based on the plan proposed by the suggestion unit. The corporate utilization unit allows a company to formulate an employee placement or development plan using methods such as reassignment, implementation of a training program, or mentoring. As a result, the AI-driven platform according to the embodiment evaluates and analyzes a user's skills and personality characteristics, proposes a career development plan, and allows a company to formulate an employee placement or development plan.

[0090] The AI-driven platform includes a reception unit that accepts a user's self-assessment test. The reception unit accepts the user's self-assessment test. Examples of self-assessment tests include, but are not limited to, personality diagnostic tests and skill assessment tests. The reception unit accepts the self-assessment test, for example, through an online form. The reception unit can also estimate the user's emotions using AI and adjust the reception method based on the estimated user emotions. For example, if the user is nervous, a simple and intuitive reception method is provided. If the user is relaxed, a reception method including detailed information is provided. If the user is in a hurry, a quick and concise reception method is provided. This allows the user's self-assessment test to be accepted.

[0091] The AI-driven platform includes an evaluation item setting unit that sets evaluation items. The evaluation items include, but are not limited to, technical skills, communication skills, and leadership. The evaluation item setting unit sets the evaluation items using methods such as survey results, expert opinions, and past data analysis. The evaluation item setting unit can also estimate the user's emotions using AI and adjust the method for setting the evaluation items based on the estimated user's emotions. For example, if the user is nervous, simple and intuitive evaluation items are set. If the user is relaxed, evaluation items including detailed information are set. If the user is in a hurry, quick and concise evaluation items are set. In this way, the evaluation items can be set.

[0092] The AI-driven platform includes a storage unit that stores the evaluation results. The storage unit stores the evaluation results. Methods for storing the evaluation results include, but are not limited to, storing them in a database or cloud storage. The storage unit stores the evaluation results in a database, for example. The storage unit can also estimate the user's emotions using AI and determine the priority of stored data based on the estimated user's emotions. For example, if the user is nervous, important data is stored first. If the user is relaxed, detailed data is stored first. If the user is in a hurry, data that needs to be stored quickly is stored first. In this way, the evaluation results can be stored.

[0093] The AI-driven platform includes a corporate interface unit for use by companies. The corporate interface unit provides an interface for use by companies. Examples of interfaces include, but are not limited to, a web interface and a mobile application. The corporate interface unit provides, for example, a dashboard for companies to view and analyze employee evaluation results. The corporate interface unit can also use AI to estimate a user's emotions and adjust the interface display method based on the estimated user's emotions. For example, if the user is nervous, a simple, highly visible interface is provided. If the user is relaxed, an interface containing detailed information is provided. If the user is in a hurry, an interface that focuses on the main points is provided. In this way, interfaces for use by companies can be provided.

[0094] The AI-driven platform includes an analysis method section that describes in detail the algorithms or analysis methods used by the AI. The analysis method section specifically describes the algorithms and analysis methods used by the AI. Examples of algorithms and analysis methods include, but are not limited to, neural networks, support vector machines, and decision trees. The analysis method section, for example, uses a neural network to analyze a user's skills and personality characteristics. The analysis method section can also use AI to estimate the user's emotions and adjust the analysis method based on the estimated user emotions. For example, if the user is nervous, a simple and intuitive analysis method is applied. If the user is relaxed, an analysis method containing detailed information is applied. If the user is in a hurry, a quick and concise analysis method is applied. This allows the algorithms and analysis methods used by the AI ​​to be specifically described.

[0095] The evaluation unit can estimate the user's emotions and adjust the order of evaluation items based on the estimated user's emotions. For example, if the user is feeling stressed, the evaluation unit can start with easy questions and gradually increase the difficulty level. If the user is relaxed, the evaluation unit can also present detailed questions first to gain deeper insight. If the user is in a hurry, the evaluation unit can also present important questions first to enable the user to complete the evaluation quickly. This makes it possible to adjust the order of evaluation items based on the user's emotions.

[0096] The evaluation unit can refer to the user's past evaluation results and build a feedback loop to improve the accuracy of the evaluation. For example, the evaluation unit adjusts the weighting of evaluation items based on the user's past evaluation results. The evaluation unit can also add questions about specific skills based on the user's past evaluation results. The evaluation unit can also analyze the user's past evaluation results and improve the algorithm for improving the accuracy of the evaluation. In this way, the accuracy of the evaluation can be improved by referring to the user's past evaluation results.

[0097] During the evaluation, the evaluation unit can customize the evaluation items based on the user's current job or life situation. For example, the evaluation unit adds questions for the user to evaluate skills related to their current job. The evaluation unit can also add questions related to stress management or time management depending on the user's life situation. The evaluation unit can also adjust the order of the evaluation items based on the user's job or life situation. This allows the evaluation items to be customized based on the user's current job or life situation.

[0098] The evaluation unit can select an appropriate evaluation means depending on the user's input method during evaluation. For example, if the user selects voice input, the evaluation unit performs evaluation using voice recognition technology. If the user selects text input, the evaluation unit can also perform evaluation using text analysis technology. If the user selects image input, the evaluation unit can also perform evaluation using image analysis technology. This makes it possible to select the optimal evaluation means depending on the user's input method.

[0099] The evaluation unit can estimate the user's emotions and adjust the method of feedback of the evaluation results based on the estimated user's emotions. For example, if the user is nervous, the evaluation unit can prioritize providing positive feedback. If the user is relaxed, the evaluation unit can also provide detailed feedback and clearly indicate areas for improvement. If the user is in a hurry, the evaluation unit can also provide concise feedback that focuses on the main points. This makes it possible to adjust the method of feedback of the evaluation results based on the user's emotions.

[0100] During evaluation, the evaluation unit can prioritize presenting highly relevant evaluation items by taking into account the user's geographical location information. For example, if the user is in a specific region, the evaluation unit can add questions that evaluate skills and knowledge related to that region. The evaluation unit can also add questions that evaluate problem-solving abilities specific to the region based on the user's geographical location information. The evaluation unit can also adjust the order of evaluation items by taking into account the user's geographical location information. This makes it possible to prioritize presenting highly relevant evaluation items by taking into account the user's geographical location information.

[0101] During the evaluation, the evaluation unit can analyze the user's social media activity and add related evaluation items. For example, the evaluation unit can analyze the content of the user's social media posts and add questions about related skills. The evaluation unit can also add evaluation items based on the user's interests and concerns from the user's social media activity. The evaluation unit can also add related evaluation items by referring to the activities of the user's friends on social media. In this way, the user's social media activity can be analyzed and related evaluation items can be added.

[0102] The evaluation unit can customize the evaluation method by reflecting the user's past feedback during evaluation. For example, the evaluation unit adjusts the weighting of evaluation items based on feedback provided by the user in the past. The evaluation unit can also add questions about specific skills based on the user's past feedback. The evaluation unit can also analyze the user's past feedback and improve the evaluation method. This makes it possible to customize the evaluation method by reflecting the user's past feedback.

[0103] The analysis unit can estimate the user's emotions and adjust the analysis algorithm based on the estimated user's emotions. For example, if the user is relaxed, the analysis unit can perform a detailed analysis and provide deep insights. If the user is in a hurry, the analysis unit can perform a quick analysis and provide results that are concise. If the user is excited, the analysis unit can provide visually stimulating analysis results. This allows the analysis algorithm to be adjusted based on the user's emotions.

[0104] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the evaluation results. For example, the analysis unit can perform a detailed analysis of important evaluation results to provide deep insight. For less important evaluation results, the analysis unit can also perform a concise analysis to provide results that focus on the main points. The analysis unit can also determine the priority of the analysis based on the importance of the evaluation results. This makes it possible to adjust the level of detail of the analysis based on the importance of the evaluation results.

[0105] During analysis, the analysis unit can apply different analysis algorithms depending on the category of the evaluation item. For example, the analysis unit applies a specific analysis algorithm to evaluation items related to problem-solving ability. The analysis unit can also apply a different analysis algorithm to evaluation items related to communication skills. The analysis unit can also apply yet another different analysis algorithm to evaluation items related to leadership characteristics. In this way, different analysis algorithms can be applied depending on the category of the evaluation item.

[0106] During analysis, the analysis unit can improve the accuracy of the analysis by referring to the user's past analysis results. For example, the analysis unit improves the analysis algorithm based on the user's past analysis results. The analysis unit can also strengthen the analysis of specific skills based on the user's past analysis results. The analysis unit can also analyze the user's past analysis results and build a feedback loop to improve the accuracy of the analysis. This makes it possible to improve the accuracy of the analysis by referring to the user's past analysis results.

[0107] The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated user's emotions. For example, if the user is nervous, the analysis unit provides a simple, highly visible display method. If the user is relaxed, the analysis unit can also provide a display method including detailed information. If the user is in a hurry, the analysis unit can also provide a display method that focuses on the main points. This makes it possible to adjust the display method of the analysis results based on the user's emotions.

[0108] During analysis, the analysis unit can determine the priority of analysis based on the submission time of the evaluation results. For example, the analysis unit prioritizes the analysis of the most recently submitted evaluation results. The analysis unit can also postpone the analysis of evaluation results that were submitted earlier. The analysis unit can also adjust the analysis schedule based on the submission time. This makes it possible to determine the priority of analysis based on the submission time of the evaluation results.

[0109] The analysis unit can adjust the order of analysis based on the relevance of the evaluation results during analysis. For example, the analysis unit prioritizes analysis of highly relevant evaluation results. The analysis unit can also postpone analysis of less relevant evaluation results. The analysis unit can also adjust the order of analysis based on the relevance of the evaluation results. This makes it possible to adjust the order of analysis based on the relevance of the evaluation results.

[0110] During analysis, the analysis unit can adjust the use of technical terms in the analysis according to the user's level of expertise. For example, if the user has technical expertise, the analysis unit can provide analysis results that make heavy use of technical terms. If the user does not have technical expertise, the analysis unit can also provide analysis results that are concise and easy to understand. The analysis unit can also adjust the way in which the analysis results are expressed based on the user's level of expertise. This makes it possible to adjust the use of technical terms in the analysis according to the user's level of expertise.

[0111] The suggestion unit can estimate the user's emotions and adjust the way in which suggestions are expressed based on the estimated user's emotions. For example, if the user is nervous, the suggestion unit makes suggestions using positive expressions. If the user is relaxed, the suggestion unit can also make suggestions that include detailed information. If the user is in a hurry, the suggestion unit can also make suggestions that are concise and to the point. In this way, the way in which suggestions are expressed can be adjusted based on the user's emotions.

[0112] When making a proposal, the proposal unit can adjust the level of detail of the proposal based on the importance of the career development plan. For example, the proposal unit makes a detailed proposal for an important career development plan. The proposal unit can also make a concise proposal for a less important career development plan. The proposal unit can also determine the priority of the proposal based on the importance of the career development plan. This makes it possible to adjust the level of detail of the proposal based on the importance of the career development plan.

[0113] When making a proposal, the suggestion unit can apply different suggestion algorithms depending on the category of the career development plan. For example, the suggestion unit applies a specific suggestion algorithm to a career development plan related to leadership development. The suggestion unit can also apply a different suggestion algorithm to a career development plan related to improving communication skills. The suggestion unit can also apply an even different suggestion algorithm to a career development plan related to improving problem-solving abilities. In this way, different suggestion algorithms can be applied depending on the category of the career development plan.

[0114] When making a suggestion, the suggestion unit can improve the accuracy of the suggestion by referring to the user's past suggestion results. For example, the suggestion unit improves the suggestion algorithm based on the user's past suggestion results. The suggestion unit can also strengthen suggestions regarding specific skills based on the user's past suggestion results. The suggestion unit can also analyze the user's past suggestion results and build a feedback loop to improve the accuracy of the suggestion. This makes it possible to improve the accuracy of the suggestion by referring to the user's past suggestion results.

[0115] 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, if the user is nervous, the suggestion unit can provide short and to-the-point suggestions. If the user is relaxed, the suggestion unit can also provide longer suggestions with detailed information. If the user is in a hurry, the suggestion unit can also provide quick and concise suggestions. This allows the length of the suggestions to be adjusted based on the user's emotions.

[0116] When making a proposal, the proposal unit can determine the priority of the proposal based on the submission date of the career development plan. For example, the proposal unit gives priority to the most recently submitted career development plan. The proposal unit can also postpone the proposal of an older submitted career development plan. The proposal unit can also adjust the proposal schedule based on the submission date. This makes it possible to determine the priority of the proposal based on the submission date of the career development plan.

[0117] The proposal unit can adjust the order of proposals based on the relevance of the career development plans when making proposals. For example, the proposal unit preferentially proposes highly relevant career development plans. The proposal unit can also postpone proposing less relevant career development plans. The proposal unit can also adjust the order of proposals based on the relevance of the career development plans. This makes it possible to adjust the order of proposals based on the relevance of the career development plans.

[0118] When making a suggestion, the suggestion unit can adjust the use of technical terminology in the suggestion according to the user's level of expertise. For example, if the user has technical expertise, the suggestion unit can make a suggestion that uses a lot of technical terminology. If the user does not have technical expertise, the suggestion unit can also make a concise and easy-to-understand suggestion. The suggestion unit can also adjust the way the suggestion is expressed based on the user's level of expertise. This makes it possible to adjust the use of technical terminology in the suggestion according to the user's level of expertise.

[0119] The corporate usage unit can estimate the user's emotions and adjust the corporate usage method based on the estimated user's emotions. For example, if the user is nervous, the corporate usage unit can provide a simple and intuitive usage method. If the user is relaxed, the corporate usage unit can also provide a usage method including detailed information. If the user is in a hurry, the corporate usage unit can also provide a quick and concise usage method. This makes it possible to adjust the corporate usage method based on the user's emotions.

[0120] When using the system for corporate use, the Corporate Use Department can analyze the employee's past evaluation results and select the optimal usage method. For example, the Corporate Use Department can propose the optimal usage method based on the employee's past evaluation results. The Corporate Use Department can also strengthen the usage method for specific skills based on the employee's past evaluation results. The Corporate Use Department can also analyze the employee's past evaluation results and improve the usage method. In this way, the optimal usage method can be selected by analyzing the employee's past evaluation results.

[0121] During corporate usage, the corporate usage department can customize the usage means based on the company's current projects and needs. For example, the corporate usage department provides usage means for assessing skills related to the company's current projects. The corporate usage department can also enhance usage means related to specific skills according to the company's needs. The corporate usage department can also adjust the order of usage means based on the company's projects and needs. This allows the usage means to be customized based on the company's current projects and needs.

[0122] The Corporate Utilization Department can improve the utilization method by reflecting the feedback from the company when the system is utilized by the company. For example, the Corporate Utilization Department adjusts the weighting of utilization methods based on the feedback from the company. The Corporate Utilization Department can also add utilization methods related to specific skills based on the feedback from the company. The Corporate Utilization Department can also analyze the feedback from the company and improve the utilization method. In this way, the utilization method can be improved by reflecting the feedback from the company.

[0123] The corporate use unit can estimate the user's emotions and determine the priority of corporate use based on the estimated user's emotions. For example, if the user is nervous, the corporate use unit can prioritize a simple and intuitive usage method. If the user is relaxed, the corporate use unit can also prioritize a usage method that includes detailed information. If the user is in a hurry, the corporate use unit can also prioritize a quick and concise usage method. In this way, the priority of corporate use can be determined based on the user's emotions.

[0124] The corporate utilization department can select the optimal utilization method in consideration of the geographical location information of the company when utilizing the company. For example, the corporate utilization department provides utilization methods that evaluate skills and knowledge specific to a region based on the geographical location information of the company. The corporate utilization department can also adjust the order of utilization methods in consideration of the geographical location information of the company. The corporate utilization department can also propose the optimal utilization method based on the geographical location information of the company. This makes it possible to select the optimal utilization method in consideration of the geographical location information of the company.

[0125] The Corporate Use Department can analyze a company's social media activity and suggest ways to use it when the company is using it. For example, the Corporate Use Department can analyze the content of a company's posts on social media and suggest ways to use it related to related skills. The Corporate Use Department can also suggest ways to use it based on interests and concerns from the company's social media activity. The Corporate Use Department can also suggest related ways to use it based on the company's social media activity. In this way, it is possible to analyze a company's social media activity and suggest ways to use it.

[0126] The Corporate Use Department can customize the usage method by reflecting the company's past feedback when the company uses the service. For example, the Corporate Use Department adjusts the weighting of usage methods based on the company's past feedback. The Corporate Use Department can also add usage methods related to specific skills based on the company's past feedback. The Corporate Use Department can also analyze the company's past feedback and improve the usage method. This makes it possible to customize the usage method by reflecting the company's past feedback.

[0127] The reception unit can estimate the user's emotions and adjust the reception method based on the estimated user's emotions. For example, if the user is nervous, the reception unit can provide a simple and intuitive reception method. If the user is relaxed, the reception unit can also provide a reception method that includes detailed information. If the user is in a hurry, the reception unit can also provide a quick and concise reception method. This makes it possible to adjust the reception method based on the user's emotions.

[0128] The reception unit can select the optimal reception method by referring to the user's past evaluation results when receiving a call. The reception unit, for example, proposes the optimal reception method based on the user's past evaluation results. The reception unit can also strengthen the reception method for a specific skill based on the user's past evaluation results. The reception unit can also analyze the user's past evaluation results and improve the reception method. In this way, the optimal reception method can be selected by referring to the user's past evaluation results.

[0129] The reception unit can customize the reception items based on the user's current living situation and areas of interest at the time of reception. For example, the reception unit adds reception items related to stress management and time management according to the user's living situation. The reception unit can also add related reception items based on the user's areas of interest. The reception unit can also adjust the order of the reception items based on the user's living situation and areas of interest. This makes it possible to customize the reception items based on the user's current living situation and areas of interest.

[0130] The reception unit can estimate the user's emotions and determine reception priorities based on the estimated user's emotions. For example, if the user is nervous, the reception unit prioritizes a simple and intuitive reception method. If the user is relaxed, the reception unit can also prioritize a reception method that includes detailed information. If the user is in a hurry, the reception unit can also prioritize a quick and concise reception method. In this way, reception priorities can be determined based on the user's emotions.

[0131] The reception unit can prioritize presenting highly relevant reception items in consideration of the user's geographical location information when receiving a request. For example, the reception unit provides reception items that evaluate region-specific skills and knowledge based on the user's geographical location information. The reception unit can also adjust the order of the reception items in consideration of the user's geographical location information. The reception unit can also suggest optimal reception items based on the user's geographical location information. This makes it possible to prioritize presenting highly relevant reception items in consideration of the user's geographical location information.

[0132] The reception unit can analyze the user's social media activity and add related reception items when receiving a request. For example, the reception unit can analyze the content of the user's social media posts and add reception items related to related skills. The reception unit can also add reception items based on the user's interests and concerns from the user's social media activity. The reception unit can also add related reception items by referring to the activities of the user's friends on social media. In this way, the user's social media activity can be analyzed and related reception items can be added.

[0133] The evaluation item setting unit can estimate the user's emotions and adjust the method for setting evaluation items based on the estimated user's emotions. For example, if the user is nervous, the evaluation item setting unit sets simple and intuitive evaluation items. If the user is relaxed, the evaluation item setting unit can also set evaluation items including detailed information. If the user is in a hurry, the evaluation item setting unit can also set quick and concise evaluation items. This makes it possible to adjust the method for setting evaluation items based on the user's emotions.

[0134] When setting evaluation items, the evaluation item setting unit can set optimal evaluation items by referring to the user's past evaluation results. The evaluation item setting unit sets optimal evaluation items, for example, based on the user's past evaluation results. The evaluation item setting unit can also add evaluation items related to specific skills from the user's past evaluation results. The evaluation item setting unit can also analyze the user's past evaluation results and improve the evaluation items. In this way, optimal evaluation items can be set by referring to the user's past evaluation results.

[0135] When setting the evaluation items, the evaluation item setting unit can customize the evaluation items based on the user's current job and lifestyle. The evaluation item setting unit sets, for example, evaluation items for evaluating skills related to the user's job. The evaluation item setting unit can also set evaluation items related to stress management and time management according to the user's lifestyle. The evaluation item setting unit can also adjust the order of the evaluation items based on the user's job and lifestyle. This makes it possible to customize the evaluation items based on the user's current job and lifestyle.

[0136] The evaluation item setting unit can estimate the user's emotions and determine the priority of the evaluation items based on the estimated user's emotions. For example, if the user is nervous, the evaluation item setting unit can prioritize simple and intuitive evaluation items. If the user is relaxed, the evaluation item setting unit can also prioritize evaluation items that include detailed information. If the user is in a hurry, the evaluation item setting unit can also prioritize quick and concise evaluation items. In this way, the priority of the evaluation items can be determined based on the user's emotions.

[0137] When setting evaluation items, the evaluation item setting unit can prioritize highly relevant evaluation items by taking into account the user's geographical location information. The evaluation item setting unit sets evaluation items that evaluate region-specific skills and knowledge, for example, based on the user's geographical location information. The evaluation item setting unit can also adjust the order of evaluation items by taking into account the user's geographical location information. The evaluation item setting unit can also suggest optimal evaluation items based on the user's geographical location information. This makes it possible to prioritize highly relevant evaluation items by taking into account the user's geographical location information.

[0138] When setting evaluation items, the evaluation item setting unit can analyze the user's social media activities and add related evaluation items. For example, the evaluation item setting unit analyzes the content of the user's posts on social media and adds evaluation items related to related skills. The evaluation item setting unit can also add evaluation items based on the user's interests and concerns from the user's social media activities. The evaluation item setting unit can also add related evaluation items by referring to the activities of the user's friends on social media. In this way, the user's social media activities can be analyzed and related evaluation items can be added.

[0139] The storage unit can estimate the user's emotions and determine the priority of stored data based on the estimated user's emotions. For example, when the user is nervous, the storage unit can prioritize storing important data. When the user is relaxed, the storage unit can also prioritize storing detailed data. When the user is in a hurry, the storage unit can also prioritize storing data that needs to be saved quickly. In this way, the priority of stored data can be determined based on the user's emotions.

[0140] The storage unit can adjust the level of detail of the storage based on the importance of the evaluation result when storing the evaluation result. For example, the storage unit stores detailed data for important evaluation results. The storage unit can also store concise data for less important evaluation results. The storage unit can also determine the priority of storage based on the importance of the evaluation result. This allows the level of detail of storage to be adjusted based on the importance of the evaluation result.

[0141] The storage unit may apply different storage algorithms depending on the category of the evaluation results when storing the results. For example, the storage unit may apply a specific storage algorithm to the evaluation results related to problem-solving ability. The storage unit may also apply a different storage algorithm to the evaluation results related to communication skills. The storage unit may also apply a further different storage algorithm to the evaluation results related to leadership traits. In this way, different storage algorithms may be applied depending on the category of the evaluation results.

[0142] The storage unit can estimate the user's emotions and adjust the display method of the stored data based on the estimated user's emotions. For example, if the user is nervous, the storage unit can provide a simple, highly visible display method. If the user is relaxed, the storage unit can also provide a display method including detailed information. If the user is in a hurry, the storage unit can also provide a display method that focuses on the main points. In this way, the display method of the stored data can be adjusted based on the user's emotions.

[0143] The storage unit can determine the priority of storage based on the submission time of the evaluation results when storing the results. For example, the storage unit prioritizes storing the most recently submitted evaluation results. The storage unit can also store older submitted evaluation results later. The storage unit can also adjust the storage schedule based on the submission time. This makes it possible to determine the priority of storage based on the submission time of the evaluation results.

[0144] The storage unit can adjust the order of storage based on the relevance of the evaluation results when storing them. For example, the storage unit prioritizes storing highly relevant evaluation results. The storage unit can also store less relevant evaluation results later. The storage unit can also adjust the order of storage based on the relevance of the evaluation results. This makes it possible to adjust the order of storage based on the relevance of the evaluation results.

[0145] The corporate interface unit can estimate the user's emotions and adjust the interface display method based on the estimated user's emotions. For example, if the user is nervous, the corporate interface unit provides a simple, highly visible interface. If the user is relaxed, the corporate interface unit can also provide an interface including detailed information. If the user is in a hurry, the corporate interface unit can also provide an interface that focuses on the main points. This makes it possible to adjust the interface display method based on the user's emotions.

[0146] When displaying the interface, the corporate interface unit can select the optimal display method by referring to the company's past operation history. For example, the corporate interface unit proposes the optimal interface display method based on the company's past operation history. The corporate interface unit can also enhance the display method for a specific function based on the company's past operation history. The corporate interface unit can also analyze the company's past operation history and improve the interface display method. This makes it possible to select the optimal display method by referring to the company's past operation history.

[0147] The enterprise interface unit can customize the display content according to the enterprise's current tasks when displaying the interface. For example, the enterprise interface unit prioritizes displaying information related to the enterprise's current tasks. The enterprise interface unit can also customize the display content of the interface according to the enterprise's tasks. The enterprise interface unit can also adjust the display order of the interface based on the enterprise's tasks. This allows the display content to be customized according to the enterprise's current tasks.

[0148] The enterprise interface unit can estimate the user's emotions and adjust the interface operation procedures based on the estimated user's emotions. For example, if the user is nervous, the enterprise interface unit can provide simple and intuitive operation procedures. If the user is relaxed, the enterprise interface unit can also provide operation procedures with detailed information. If the user is in a hurry, the enterprise interface unit can also provide quick and concise operation procedures. In this way, the interface operation procedures can be adjusted based on the user's emotions.

[0149] The corporate interface unit can select the optimal display method when displaying an interface, taking into account the device information of the company. The corporate interface unit provides a display method that matches the screen size of the device used by the company, for example. The corporate interface unit can also propose the optimal interface display method based on the device information of the company. The corporate interface unit can also adjust the display order of the interface, taking into account the device information of the company. This makes it possible to select the optimal display method, taking into account the device information of the company.

[0150] The corporate interface unit can make the display content multilingual when displaying the interface according to the language setting of the company. For example, the corporate interface unit automatically sets the display language of the interface based on the language setting of the company. The corporate interface unit can also provide a language switching function when the company uses multiple languages. If the company selects a specific language, the corporate interface unit can also display the interface in that language. This makes it possible to make the display content multilingual according to the language setting of the company.

[0151] The analysis method unit can estimate the user's emotions and adjust the analysis method based on the estimated user's emotions. For example, if the user is nervous, the analysis method unit applies a simple and intuitive analysis method. If the user is relaxed, the analysis method unit can also apply an analysis method that includes detailed information. If the user is in a hurry, the analysis method unit can also apply a quick and concise analysis method. This makes it possible to adjust the analysis method based on the user's emotions.

[0152] When setting an analysis method, the analysis method unit can adjust the level of detail of the analysis method based on the importance of the evaluation results. For example, the analysis method unit applies a detailed analysis method to important evaluation results. The analysis method unit can also apply a simple analysis method to evaluation results with low importance. The analysis method unit can also determine the priority of the analysis methods based on the importance of the evaluation results. This makes it possible to adjust the level of detail of the analysis method based on the importance of the evaluation results.

[0153] When setting the analysis method, the analysis method unit can apply different analysis methods depending on the category of the evaluation item. For example, the analysis method unit applies a specific analysis method to evaluation items related to problem-solving ability. The analysis method unit can also apply a different analysis method to evaluation items related to communication skills. The analysis method unit can also apply an even different analysis method to evaluation items related to leadership characteristics. In this way, different analysis methods can be applied depending on the category of the evaluation item.

[0154] The analysis method unit can estimate the user's emotions and adjust the display method of the analysis method based on the estimated user's emotions. For example, if the user is nervous, the analysis method unit provides a simple, highly visible display method. If the user is relaxed, the analysis method unit can also provide a display method including detailed information. If the user is in a hurry, the analysis method unit can also provide a display method that focuses on the main points. This makes it possible to adjust the display method of the analysis method based on the user's emotions.

[0155] When setting the analysis methods, the analysis method unit can determine the priority of the analysis methods based on the submission time of the evaluation results. For example, the analysis method unit prioritizes the analysis of the most recently submitted evaluation results. The analysis method unit can also postpone the analysis of evaluation results that were submitted earlier. The analysis method unit can also adjust the schedule of the analysis methods based on the submission time. This makes it possible to determine the priority of the analysis methods based on the submission time of the evaluation results.

[0156] When setting the analysis methods, the analysis method unit can adjust the order of the analysis methods based on the relevance of the evaluation results. For example, the analysis method unit prioritizes analysis of highly relevant evaluation results. The analysis method unit can also postpone analysis of less relevant evaluation results. The analysis method unit can also adjust the order of the analysis methods based on the relevance of the evaluation results. This makes it possible to adjust the order of the analysis methods based on the relevance of the evaluation results.

[0157] When setting an analysis method, the analysis method unit can adjust the use of technical terms in the analysis method according to the user's level of expertise. The analysis method unit adjusts the use of technical terms in the analysis method according to, for example, the user's level of expertise. If the user has technical knowledge, the analysis method unit can also apply an analysis method that makes heavy use of technical terms. If the user does not have technical knowledge, the analysis method unit can also apply a simple and easy-to-understand analysis method. This makes it possible to adjust the use of technical terms in the analysis method according to the user's level of expertise. === Hard Collateral 1-1 === For example, each of the multiple elements including the evaluation unit, analysis unit, proposal unit, corporate utilization unit, reception unit, evaluation item setting unit, storage unit, corporate interface unit, and analysis method unit is realized by at least one of the smart device 14 and the data processing device 12. For example, the evaluation unit is realized by the control unit 46A of the smart device 14 and evaluates the user's skills and personality characteristics. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the evaluation results. The proposal unit is realized by the specific processing unit 290 of the data processing device 12 and proposes a career development plan based on the analysis results. The corporate utilization unit is realized by the specific processing unit 290 of the data processing device 12 and allows companies to formulate employee placement and training plans. The reception unit is realized by the control unit 46A of the smart device 14 and accepts the user's self-evaluation test. The evaluation item setting unit is realized by the specific processing unit 290 of the data processing device 12 and sets evaluation items. The storage unit is realized by the specific processing unit 290 of the data processing device 12 and saves the evaluation results. The company interface unit is realized by the control unit 46A of the smart device 14 and provides an interface for use by companies. The analysis method unit is realized by the specific processing unit 290 of the data processing device 12 and specifically describes the algorithms and analysis methods used by the AI. === Hard Collateral 1-2 === For example, each of a plurality of elements including an evaluation unit, an analysis unit, a proposal unit, a corporate utilization unit, a reception unit, an evaluation item setting unit, a storage unit, a corporate interface unit, and an analysis method unit is realized by at least one of the smart glasses 214 and the data processing device 12. For example, the evaluation unit is realized by the control unit 46A of the smart glasses 214 and evaluates the user's skills and personality characteristics. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the evaluation results. The proposal unit is realized by the specific processing unit 290 of the data processing device 12 and proposes a career development plan based on the analysis results. The corporate utilization unit is realized by the specific processing unit 290 of the data processing device 12 and allows companies to formulate employee placement and training plans. The reception unit is realized by the control unit 46A of the smart glasses 214 and accepts a user's self-evaluation test. The evaluation item setting unit is realized by the specific processing unit 290 of the data processing device 12 and sets evaluation items. The storage unit is realized by the specific processing unit 290 of the data processing device 12 and stores the evaluation results. The company interface unit is realized by the control unit 46A of the smart glasses 214 and provides an interface for use by companies. The analysis method unit is realized by the specific processing unit 290 of the data processing device 12 and specifically describes the algorithms and analysis methods used by the AI. === Hard Collateral 1-3 === For example, each of a plurality of elements including an evaluation unit, an analysis unit, a proposal unit, a corporate utilization unit, a reception unit, an evaluation item setting unit, a storage unit, a corporate interface unit, and an analysis method unit is realized by at least one of the headset type terminal 314 and the data processing device 12. For example, the evaluation unit is realized by the control unit 46A of the headset type terminal 314 and evaluates the user's skills and personality characteristics. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the evaluation results. The proposal unit is realized by the specific processing unit 290 of the data processing device 12 and proposes a career development plan based on the analysis results. The corporate utilization unit is realized by the specific processing unit 290 of the data processing device 12 and allows a company to formulate employee placement and training plans. The reception unit is realized by the control unit 46A of the headset type terminal 314 and accepts a user's self-evaluation test. The evaluation item setting unit is realized by the specific processing unit 290 of the data processing device 12 and sets evaluation items. The storage unit is realized by the specific processing unit 290 of the data processing device 12 and stores the evaluation results. The company interface unit is realized by the control unit 46A of the headset type terminal 314 and provides an interface for use by companies. The analysis method unit is realized by the specific processing unit 290 of the data processing device 12 and specifically describes the algorithms and analysis methods used by the AI. === Hard Collateral 1-4 === For example, each of a plurality of elements including an evaluation unit, an analysis unit, a proposal unit, a corporate utilization unit, a reception unit, an evaluation item setting unit, a storage unit, a corporate interface unit, and an analysis method unit is realized by at least one of the robot 414 and the data processing device 12. For example, the evaluation unit is realized by the control unit 46A of the robot 414 and evaluates the user's skills and personality characteristics. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the evaluation results. The proposal unit is realized by the specific processing unit 290 of the data processing device 12 and proposes a career development plan based on the analysis results. The corporate utilization unit is realized by the specific processing unit 290 of the data processing device 12 and allows companies to formulate employee placement and training plans. The reception unit is realized by the control unit 46A of the robot 414 and accepts a user's self-evaluation test. The evaluation item setting unit is realized by the specific processing unit 290 of the data processing device 12 and sets evaluation items. The storage unit is realized by the specific processing unit 290 of the data processing device 12 and stores the evaluation results. The company interface unit is realized by the control unit 46A of the robot 414 and provides an interface for use by companies. The analysis method unit is realized by the specific processing unit 290 of the data processing device 12 and specifically describes the algorithms and analysis methods used by the AI.

[0158] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0159] The analysis unit can create a feedback loop to improve the accuracy of the evaluation based on the user's past evaluation results. For example, it can strengthen questions about skills for which the user has previously received high ratings and perform a more detailed analysis. It can also clearly indicate areas for improvement and provide specific advice for skills for which the user has previously received low ratings. Furthermore, by tracking the user's evaluation results over time and analyzing skill growth and changes, it can propose more accurate career development plans.

[0160] The reception unit can refer to the user's past evaluation results and select the optimal reception method. For example, if the user has been nervous in the past, a simple and intuitive reception method can be provided. Also, if the user has been relaxed in the past, a reception method including detailed information can be provided. Furthermore, if the user has been in a hurry in the past, a quick and simple reception method can be provided. In this way, the optimal reception method can be selected based on the user's past evaluation results.

[0161] The evaluation item setting unit can customize the evaluation items based on the user's current job and life situation. For example, it can add questions to evaluate skills related to the user's current job. It can also add questions related to stress management and time management depending on the user's life situation. It can also adjust the order of the evaluation items based on the user's job and life situation. This allows the evaluation items to be customized based on the user's current job and life situation.

[0162] The storage unit can adjust the level of detail of storage based on the importance of the evaluation results. For example, detailed data can be stored for important evaluation results. Also, brief data can be stored for evaluation results with low importance. Furthermore, the priority of storage can be determined based on the importance of the evaluation results. In this way, the level of detail of storage can be adjusted based on the importance of the evaluation results.

[0163] The enterprise interface unit can customize the utilization means based on the current projects and needs of the enterprise. For example, it can provide utilization means for assessing skills related to the current projects of the enterprise. It can also enhance utilization means related to specific skills according to the needs of the enterprise. It can also adjust the order of utilization means based on the projects and needs of the enterprise. This allows the utilization means to be customized based on the current projects and needs of the enterprise.

[0164] The evaluation unit can estimate the user's emotions and adjust the order of evaluation items based on the estimated user's emotions. For example, if the user is feeling stressed, it can start with easy questions and gradually increase the difficulty level. If the user is relaxed, it can present detailed questions first to gain deeper insights. Furthermore, if the user is in a hurry, it can present important questions first to enable the user to complete the evaluation quickly. In this way, it is possible to adjust the order of evaluation items based on the user's emotions.

[0165] The evaluation unit can estimate the user's emotions and adjust the method of providing feedback on the evaluation results based on the estimated user's emotions. For example, if the user is nervous, positive feedback can be given priority. If the user is relaxed, detailed feedback can be provided and areas for improvement can be clearly indicated. Furthermore, if the user is in a hurry, brief feedback that focuses on the main points can be provided. In this way, the method of providing feedback on the evaluation results can be adjusted based on the user's emotions.

[0166] The analysis unit can estimate the user's emotions and adjust the analysis algorithm based on the estimated user's emotions. For example, if the user is relaxed, a detailed analysis can be performed to provide deep insights. If the user is in a hurry, a quick analysis can be performed to provide results that are concise. Furthermore, if the user is excited, a visually stimulating analysis result can be provided. This allows the analysis algorithm to be adjusted based on the user's emotions.

[0167] The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated user emotions. For example, if the user is nervous, a simple, highly visible display method can be provided. If the user is relaxed, a display method including detailed information can be provided. Furthermore, if the user is in a hurry, a display method that focuses on the main points can be provided. In this way, the display method of the analysis results can be adjusted based on the user's emotions.

[0168] The suggestion unit can estimate the user's emotions and adjust the way suggestions are expressed based on the estimated user's emotions. For example, if the user is nervous, the suggestion unit can make suggestions using positive expressions. If the user is relaxed, the suggestion unit can make suggestions including detailed information. Furthermore, if the user is in a hurry, the suggestion unit can make suggestions that are concise and to the point. In this way, the way suggestions are expressed can be adjusted based on the user's emotions.

[0169] The processing flow of the second embodiment will be briefly explained below.

[0170] Step 1: The evaluation unit evaluates the user's skills or personality traits. User skills include technical skills, communication skills, leadership skills, etc., while personality traits include extroversion, cooperativeness, conscientiousness, etc. The evaluation unit can use methods such as questionnaires, interviews, and tests to conduct the evaluation, and can also use AI to estimate the user's emotions and adjust the order of the evaluation items. Step 2: The analysis unit analyzes the evaluation results obtained by the evaluation unit. The analysis is performed using methods such as statistical analysis, data mining, and machine learning, and can refer to the user's past evaluation results and create a feedback loop to improve the accuracy of the evaluation. Step 3: The proposal unit proposes a career development plan based on the analysis results obtained by the analysis unit. The career development plan may include training programs, promotion plans, skill improvement plans, etc. The proposal unit can estimate the user's emotions and adjust the way the proposal is presented. Step 4: The Corporate Utilization Department formulates employee placement or development plans based on the plans proposed by the Proposal Department. The Corporate Utilization Department can help companies formulate employee placement or development plans using methods such as transfers, training programs, and mentoring.

[0171] 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.

[0172] 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 generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. 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 a data format such as voice data and text data. 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 can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0173] 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.

[0174] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0175] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0176] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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).

[0181] 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.

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] 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 containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. 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 a data format such as voice data and text 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 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 can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0189] 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.

[0190] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0191] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0192] 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.

[0193] 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.

[0194] 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.

[0195] 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.

[0196] 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).

[0197] 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.

[0198] 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.

[0199] 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.

[0200] 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.

[0201] 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.

[0202] 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.

[0203] 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.

[0204] 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 containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. 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 a data format such as voice data and text 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 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 can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0205] 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.

[0206] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0207] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0208] 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0209] 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.

[0210] 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.

[0211] 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.

[0212] 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).

[0213] 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.

[0214] 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.

[0215] 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.

[0216] 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.

[0217] 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.

[0218] 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.

[0219] 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.

[0220] 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.

[0221] 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 containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. 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 a data format such as voice data and text 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 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 can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0222] 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.

[0223] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0224] 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.

[0225] 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.

[0226] 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.

[0227] 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).

[0228] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0229] 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."

[0230] 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.

[0231] 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.

[0232] 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.

[0233] 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.

[0234] 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.

[0235] 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.

[0236] 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.

[0237] 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.

[0238] 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.

[0239] 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.

[0240] 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.

[0241] 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.

[0242] [Explanation of symbols]

[0243] 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. an evaluation unit for evaluating the skills or personality characteristics of the user; an analysis unit that analyzes the evaluation results obtained by the evaluation unit; a proposal unit that proposes a career development plan based on the analysis results obtained by the analysis unit; a company utilization department that formulates employee placement or training plans for companies based on the plans proposed by the proposal department; Equipped with A system characterized by:

2. A reception unit is provided to accept the user's self-assessment test.

2. The system of claim 1.

3. Equipped with an evaluation item setting unit that sets evaluation items 2. The system of claim 1.

4. Equipped with a storage unit for storing evaluation results 2. The system of claim 1.

5. Equipped with a corporate interface section for use by companies 2. The system of claim 1.

6. It has an analysis method section that details the algorithms or analysis methods used by the AI.

2. The system of claim 1.

7. The evaluation unit Infer user sentiment and adjust the order of evaluation items based on the estimated user sentiment 2. The system of claim 1.

8. The evaluation unit Build a feedback loop to improve the accuracy of your ratings by referencing your past ratings.

2. The system of claim 1.

9. The evaluation unit Customize assessment items based on the user's current job or life situation during assessment 2. The system of claim 1.

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